Machine Learning Engineer Jobs in New York, USA

<div> <p style="margin-top: 0pt; margin-bottom: 0pt;"><strong><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">Who We Are:</span></strong></p> <p style="margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">The world is electrifying, and HVAC is at the center of it. Over the next decade, 100 to 200 million new heat pumps and HVAC units will become the backbone of a decarbonized world, but the industry has no way to keep them running well. The technician workforce has barely grown while the equipment base has multiplied, reactive repairs eat most of a tech's time, and half the installed base gets no real maintenance at all, wasting energy and driving billions in emergency costs. Thalo is fixing this. We're building the physical AI layer for HVAC: sensors plus physics-based intelligence that turn static equipment into self-monitoring systems and shift service from guesswork to data. Every sensor we deploy makes the platform smarter and builds a dataset on how equipment truly performs that no one else can.</span></p> <br> <p style="margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">We're a small team that has built self-driving cars at Waymo, worked on satellite imagery at Google, designed systems for John Deere, developed space missions for NASA, and led manufacturing design for Boom Supersonic jets. Now we're bringing that same rigor to one of the most important buildouts of our lifetime. In this role, the models you build decide whether a technician is sent to the right unit at the right time, and whether a building wastes energy or runs clean. It's a rare chance to work on a generational climate challenge, with first-of-its-kind data and a team of high performers who ship.</span></p> <br><br> <p style="margin-top: 0pt; margin-bottom: 0pt;"><strong><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">About the Role:</span></strong></p> <p style="margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">As our Senior Machine Learning Engineer, you’ll own the intelligence layer of Thalo’s platform. We generate hundreds of gigabytes of HVAC sensor data no one in the world has seen before, and your job is to turn it into the detection algorithms, physics-based models, and product features that tell our customers exactly what’s wrong with their equipment and what to do about it.&nbsp;&nbsp;</span></p> <br> <p style="margin-top: 0pt; margin-bottom: 0pt;"><span style="font-size: 11pt; font-family: 'Inter Tight', sans-serif;">This is a hands-on, end-to-end role for someone who wants to own a problem from raw time-series data all the way to a shipped, customer-facing feature. You’ll build and tune our issue-detection engine, put physics-based, ML, and LLM-powered models into production, establish how we evaluate and trust them, and work closely with our engineering, customer success, and business development teams to make sure the intelligence we ship is accurate, trustworthy, and genuinely useful in the field. You’ll be a senior voice on a small, mighty team!</span></p> </div>

<p><b>About this role</b><br><br>At BlackRock, technology is fundamental to how we serve clients and operate at scale. Aladdin-our industry-leading, end-to-end investment management platform-embodies this by combining deep financial expertise with advanced data and AI capabilities. <br><br>We are seeking a Director of AI Platform / Engineering to provide technical and strategic leadership within the Aladdin AI Engineering organization. In this role, you will be responsible for shaping the direction of Aladdin's AI platform, leading high-impact engineering initiatives, and ensuring the delivery of scalable, reliable, and responsible AI solutions across investment, operations, and data science workflows. <br><br>This role requires a blend of hands-on technical depth, architectural leadership, and people and delivery leadership. You will work closely with senior engineering leaders, product managers, data scientists, and business stakeholders to translate AI strategy into production-grade platforms and services. <br><br><b>Key Responsibilities</b> <br><br><b>Technical &amp; Architectural Leadership</b> <br><br><ul><li>Set the <b>t</b>echnical vision and architecture for Aladdin's AI platform, including LLM-powered and agentic AI systems.</li></ul><ul><li>Lead the design and evolution of scalable AI/ML platform services, APIs, and microservices that support Aladdin's global user base.</li></ul><ul><li>Drive architectural decisions across distributed systems, ensuring reliability, scalability, security, and operational excellence.</li></ul><ul><li>Establish and enforce engineering standards, design patterns, and best practices across AI platform teams.</li></ul><ul><li>Ensure AI solutions are built with strong considerations for governance, observability, resilience, and responsible AI.</li></ul><br><br><b>Delivery &amp; Execution</b> <br><br><ul><li>Own delivery outcomes for multiple concurrent initiatives, balancing near-term execution with long-term platform evolution.</li></ul><ul><li>Partner with product management to define roadmaps, prioritize investments, and align execution with business outcomes.</li></ul><ul><li>Oversee production readiness, including design reviews, risk assessments, release management, and operational support.</li></ul><ul><li>Drive continuous improvement in engineering workflows, quality, and delivery predictability.</li></ul><br><br><b>People &amp; Organizational Leadership</b> <br><br><ul><li>Lead, mentor, and develop senior engineers, technical leads, and managers.</li></ul><ul><li>Build a strong engineering culture centered on ownership, rigor, collaboration, and technical excellence.</li></ul><ul><li>Support hiring, onboarding, performance management, and succession planning.</li></ul><ul><li>Act as a senior technical voice across leadership forums and architectural reviews.</li></ul><br><br><b>Stakeholder &amp; Cross-Functional Collaboration</b> <br><br><ul><li>Partner closely with platform, product, data science, and infrastructure leaders.</li></ul><ul><li>Communicate complex technical concepts clearly to senior leadership and non-technical stakeholders.</li></ul><ul><li>Represent Aladdin AI Engineering in cross-organizational initiatives and governance forums.</li></ul><br><br><b>Required Qualifications</b> <br><br><b>AI / ML-Specific Qualifications</b> <br><br><ul><li>Proven experience leading teams that build <b>AI-driven platforms and applications</b>, including solutions leveraging <b>large language models (LLMs),</b> AI Agents, agent tools and agent skills.</li></ul><ul><li>Strong hands-on familiarity with <b>LLM frameworks</b> such as <b>LangChain, LlamaIndex, Semantic Kernel</b>, or equivalent.</li></ul><ul><li><b>Experience with Google Agent Development Kit (ADK)</b> or comparable agent frameworks, including designing, orchestrating, and operating agent-based systems in production.</li></ul><ul><li>Deep understanding of <b>agentic AI architectures</b>, including multi-agent coordination, tool invocation, and stateful reasoning workflows.</li></ul><ul><li>Experience with <b>prompt engineering and prompt tuning</b> at scale, including performance, reliability, and safety optimization.</li></ul><ul><li>Strong understanding of <b>ML model evaluation</b>, monitoring, and behavioral drift management in production systems.</li></ul><ul><li>Experience integrating <b>vector databases</b> (e.g., Faiss, Chroma) into AI system architectures.</li></ul><ul><li>Solid understanding of <b>MLOps practices</b>, including model lifecycle management, deployment pipelines, and observability.</li></ul><ul><li>Experience leveraging AInative code editors and IDEs (e.g., Copilotenabled IDEs, agentassisted development environments, or similar AIaugmented tooling) to improve developer productivity, code quality, and delivery velocity.</li></ul><ul><li>Experience establishing best practices and guardrails for the responsible use of AInative development tools, including considerations for security, IP protection, compliance, and code quality.</li></ul><ul><li>Ability to evaluate and guide adoption of emerging AIpowered developer tools, balancing innovation with maintainability, governance, and platform standards.</li></ul><ul><li>Strong awareness of <b>responsible AI principles</b>, governance, and risk management when deploying AI systems in regulated environments.</li></ul><br><br><b>Core Engineering &amp; Leadership Qualifications</b> <br><br><ul><li>B.S. or M.S. in Computer Science, Engineering, or a related field.</li></ul><ul><li><b>10+ years of software engineering experience</b>, including significant leadership of large-scale, distributed systems.</li></ul><ul><li>Proven track record of <b>technical leadership</b>, owning platform architecture and multi-team delivery.</li></ul><ul><li>Deep expertise in <b>object-oriented programming</b>, particularly <b>Java and Python</b>.</li></ul><ul><li>Strong experience designing and operating <b>scalable APIs, microservices, and distributed systems</b> in production.</li></ul><ul><li>Extensive experience with <b>cloud platforms</b> (Azure preferred; AWS or GCP acceptable).</li></ul><ul><li>Strong understanding of <b>event-driven architectures</b>, messaging systems (e.g., Kafka), and asynchronous processing.</li></ul><ul><li>Experience with <b>containerization and orchestration technologies</b> such as Docker and Kubernetes.</li></ul><ul><li>Hands-on experience with <b>relational and NoSQL datastores</b>, including Cassandra.</li></ul><ul><li>Strong grasp of <b>Agile delivery models</b>, SDLC governance, and operational excellence.</li></ul><ul><li>Demonstrated ability to influence across teams and senior leadership.</li></ul><br><br><b>Why BlackRock</b> <br><br>Joining BlackRock means becoming part of a firm that oversees more than <b>$14 trillion in assets</b> and operates at the intersection of finance and technology. As a Director in Aladdin AI Engineering, you will play a critical role in shaping the future of AI-powered investment management at global scale. <br><br>For New York, NY Only the salary range for this position is USD$240,000.00 - USD$300,000.00 . Additionally, employees are eligible for an annual discretionary bonus, and benefits including healthcare, leave benefits, and retirement benefits. BlackRock operates a pay-for-performance compensation philosophy and your total compensation may vary based on role, location, and firm, department and individual performance.<br/> <br><br><b>Our benefits</b><br><br>To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.<br><br><b>Our hybrid work model</b><br><br>BlackRock's hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our em

<p>Lead Machine Learning Engineer (Enterprise Platforms Technology)<br><br>As a Capital One Machine Learning Engineer (MLE), you'll be part of an Agile team dedicated to productionizing machine learning applications and systems at scale. You'll participate in the detailed technical design, development, and implementation of machine learning applications using existing and emerging technology platforms. You'll focus on machine learning architectural design, develop and review model and application code, and ensure high availability and performance of our machine learning applications. You'll have the opportunity to continuously learn and apply the latest innovations and best practices in machine learning engineering. <br><br><b>What you'll do in the role: </b><br><br>The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:<br><ul><li>Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams.</li><li>Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation).</li><li>Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment.</li><li>Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications.</li><li>Retrain, maintain, and monitor models in production.</li><li>Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</li><li>Construct optimized data pipelines to feed ML models.</li><li>Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code.</li><li>Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI.</li><li>Use programming languages like Python, Scala, or Java.</li></ul><br><br><b>Basic Qualifications:</b><br><ul><li>Bachelor's Degree</li><li>At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</li><li>At least 4 years of experience programming with Python, Scala, or Java</li><li>At least 2 years of experience building, scaling, and optimizing ML systems</li></ul><br><br><b>Preferred Qualifications:</b><br><ul><li>Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field</li><li>3+ years of experience building production-ready data pipelines that feed ML models</li><li>3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow</li><li>2+ years of experience developing performant, resilient, and maintainable code</li><li>2+ years of experience with data gathering and preparation for ML models</li><li>2+ years of people leader experience</li><li>1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation</li><li>Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</li><li>Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance</li><li>ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents</li></ul><br><br><b>At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).</b><br><br>The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.<br><br>McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer<br><br>New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer<br><br>Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.<br><br>This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.<br><br>Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.<br><br>This role is expected to accept applications for a minimum of 5 business days.<br><br>No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.<br><br>If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.<br><br>For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com<br><br>Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.<br><br>Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).</p>

<p>Lead Machine Learning Engineer (Manager IC)<br><br><b>As a Capital One Machine Learning Engineer (MLE), you'll join an Agile team building and productionizing <b>foundation models</b> at scale. Our work centers on <b>self-supervised learning for transformer architectures</b> - pretraining on Capital One's rich, large-scale behavioral data to learn representations that power applications across use cases such as <b>fraud, marketing, and servicing</b>. You'll participate in the detailed technical design, development, and implementation of these systems, spanning model architecture, large-scale training and representation learning, and the engineering required to serve models reliably in production. You'll develop and review model and application code, drive machine learning architectural decisions, and ensure the high availability and performance of our applications. And you'll have the opportunity to continuously learn and apply the latest innovations in self-supervised learning, transformer modeling, and ML engineering best practices.</b><br><br><b><b>What You'll Do:</b></b><br><br><b>The MLE role overlaps with many disciplines, such as Ops, Modeling, and Data Engineering. In this role, you'll be expected to perform many ML engineering activities, including one or more of the following:</b><br><ul><li><b>Design, build, and/or deliver ML models and components that solve real-world business problems, while working in collaboration with the Product and Data Science teams</b></li><li><b>Inform your ML infrastructure decisions using your understanding of ML modeling techniques and issues, including choice of model, data, and feature selection, model training, hyperparameter tuning, dimensionality, bias/variance, and validation)</b></li><li><b>Solve complex problems by writing and testing application code, developing and validating ML models, and automating tests and deployment</b></li><li><b>Collaborate as part of a cross-functional Agile team to create and enhance software that enables state-of-the-art big data and ML applications</b></li><li><b>Retrain, maintain, and monitor models in production</b></li><li><b>Leverage or build cloud-based architectures, technologies, and/or platforms to deliver optimized ML models at scale.</b></li><li><b>Construct optimized data pipelines to feed ML models</b></li><li><b>Leverage continuous integration and continuous deployment best practices, including test automation and monitoring, to ensure successful deployment of ML models and application code</b></li><li><b>Ensure all code is well-managed to reduce vulnerabilities, models are well-governed from a risk perspective, and the ML follows best practices in Responsible and Explainable AI</b></li><li><b>Use programming languages like Python, Scala, or Java</b></li></ul><br><br><b><b>Basic Qualifications:</b></b><br><ul><li><b>Bachelor's Degree </b></li><li><b>At least 6 years of experience designing and building data-intensive solutions using distributed computing (Internship experience does not apply)</b></li><li><b>At least 4 years of experience programming with Python, Scala, or Java</b></li><li><b>At least 2 years of experience building, scaling, and optimizing ML systems</b></li></ul><br><br><b><b>Preferred Qualifications:</b></b><br><ul><li><b>Master's or Doctoral Degree in computer science, electrical engineering, mathematics, or a similar field</b></li><li><b>3+ years of experience building production-ready data pipelines that feed ML models </b></li><li><b>3+ years of on-the-job experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow </b></li><li><b>2+ years of experience developing performant, resilient, and maintainable code</b></li><li><b>2+ years of experience with data gathering and preparation for ML models</b></li><li><b>2+ years of people leader experience</b></li><li><b>1+ years of experience leading teams developing ML solutions using industry best practices, patterns, and automation </b></li><li><b>Experience developing and deploying ML solutions in a public cloud such as AWS, Azure, or Google Cloud Platform</b></li><li><b>Experience designing, implementing, and scaling complex data pipelines for ML models and evaluating their performance </b></li><li><b>ML industry impact through conference presentations, papers, blog posts, open source contributions, or patents </b></li><li><b>Experience leveraging interactive AI tooling to accelerate productivity, utilizing capabilities beyond basic code completion</b></li></ul><br><b>At this time, Capital One will not sponsor a new applicant for employment authorization, or offer any immigration related support for this position (i.e. H1B, F-1 OPT, F-1 STEM OPT, F-1 CPT, J-1, TN, E-2, E-3, L-1 and O-1, or any EADs or other forms of work authorization that require immigration support from an employer).</b><br><br>The minimum and maximum full-time annual salaries for this role are listed below, by location. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Capital One is willing to pay at the time of this posting. Salaries for part-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.<br><br>Cambridge, MA: $197,300 - $225,100 for Lead Machine Learning Engineer<br><br>McLean, VA: $197,300 - $225,100 for Lead Machine Learning Engineer<br><br>New York, NY: $215,200 - $245,600 for Lead Machine Learning Engineer<br><br>San Jose, CA: $215,200 - $245,600 for Lead Machine Learning Engineer<br><br>Candidates hired to work in other locations will be subject to the pay range associated with that location, and the actual annualized salary amount offered to any candidate at the time of hire will be reflected solely in the candidate's offer letter.<br><br>This role is also eligible to earn performance based incentive compensation, which may include cash bonus(es) and/or long term incentives (LTI). Incentives could be discretionary or non discretionary depending on the plan.<br><br>Capital One offers a comprehensive, competitive, and inclusive set of health, financial and other benefits that support your total well-being. Learn more at the Capital One Careers website. Eligibility varies based on full or part-time status, exempt or non-exempt status, and management level.<br><br>This role is expected to accept applications for a minimum of 5 business days.<br><br>No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City's Fair Chance Act; Philadelphia's Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries.<br><br>If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations.<br><br>For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com<br><br>Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site.<br><br>Capi

<p><b><u>Disney Entertainment and ESPN Product &amp; Technology</u></b></p> <p>Technology is at the heart of Disney’s past, present, and future. Disney Entertainment and ESPN Product &amp; Technology is a global organization of engineers, product developers, designers, technologists, data scientists, and more – all working to build and advance the technological backbone for Disney’s media business globally.</p> <p></p> <p>The team marries technology with creativity to build world-class products, enhance storytelling, and drive velocity, innovation, and scalability for our businesses.&nbsp;We are Storytellers and Innovators. Creators and Builders. Entertainers and Engineers. We work with every part of The Walt Disney Company’s media portfolio to advance the technological foundation and consumer media touch points serving millions of people around the world.&nbsp;</p> <p></p> <p>Here are a few reasons why we think you’d love working here:</p> <p></p> <p><b>1. Building the future of Disney’s media: </b>Our Technologists are designing and building the products and platforms that will power our media, advertising, and distribution businesses for years to come.</p> <p></p> <p><b>2. Reach, Scale &amp; Impact:</b> More than ever, Disney’s technology and products serve as a signature doorway for fans' connections with the company’s brands and stories. Disney+. Hulu. ESPN. ABC. ABC News…and many more. These products and brands – and the unmatched stories, storytellers, and events they carry – matter to millions of people globally.&nbsp;</p> <p></p> <p><b>3. Innovation:</b> We develop and implement groundbreaking products and techniques that shape industry norms and solve complex and distinctive technical problems.</p> <p></p> <p><b>Job Summary:</b></p> <p></p> <p>Product Engineering is a unified team responsible for the engineering of Disney Entertainment &amp; ESPN digital and streaming products and platforms. This includes product engineering, media engineering, quality assurance, engineering behind personalization, commerce, lifecycle, and identity.</p> <p></p> <p>ESPN is building the next-generation video experience for our global streaming platform, and personalization will be at the core of delivering a world-class user experience. We are seeking a Principal Machine Learning Engineer to serve as the technical architect and driving force behind the design, development, and deployment of our real-time recommendation engine. This is a unique opportunity to lead the technical direction and build foundational personalization capabilities that will directly shape user engagement, satisfaction, and long-term growth.</p> <p></p> <p>In this role, you will partner closely with engineering, product, data science, and business teams to define system architecture, design large-scale ML solutions, and drive end-to-end ownership of real-time recommendation systems from 0 to 1. You will bring deep technical expertise in recommendation algorithms, real-time serving architectures, and large-scale machine learning systems, as well as the leadership and communication skills to influence cross-functional teams.</p> <p></p> <p><b>Responsibilities and Duties of the Role:</b></p> <ul><li><p>Serve as the technical architect and primary owner for the design and implementation of ESPN’s real-time short-form video recommendation system.</p></li><li><p>Design, develop, and deploy large-scale, end-to-end ML pipelines for real-time retrieval, ranking, and personalization at scale.</p></li><li><p>Lead research, prototyping, and product ionization of cutting-edge recommendation algorithms, leveraging deep learning, embeddings, sequence models, transformers, and multi-task learning.</p></li><li><p>Define system architecture for low-latency online inference, streaming data pipelines, feature stores, and online/offline model serving.</p></li><li><p>Collaborate with cross-functional stakeholders to define personalization strategies, system requirements, metrics, and experimentation frameworks to drive continuous improvement.</p></li><li><p>Lead complex technical discussions and make high-impact design decisions balancing model quality, scalability, system latency, and operational reliability.</p></li><li><p>Establish ML engineering best practices, development standards, and model governance processes to ensure robust, reliable, and reproducible ML systems.</p></li><li><p>Mentor and coach other machine learning engineers, helping to grow technical capability across the team and broader organization.</p></li><li><p>Stay current with state-of-the-art research and industry trends; proactively incorporate emerging technologies into ESPN’s personalization roadmap.</p></li></ul> <p></p> <p><b>Required Education, Experience/Skills/Training:</b><br>Basic Qualifications:</p> <ul><li><p>Proven track record of designing and deploying real-time, large-scale ML recommendation systems (preferably in consumer or streaming platforms).</p></li><li><p>Strong expertise in machine learning algorithms, deep learning architectures (e.g., sequence models, transformers, embeddings, multi-task learning), and personalization methodologies.</p></li><li><p>Deep understanding of real-time serving architectures, online inference, feature stores, streaming data pipelines, and low-latency ML systems.</p></li><li><p>Proficiency in Python and common ML frameworks (e.g., TensorFlow, PyTorch, ONNX), and experience integrating ML models into production services.</p></li><li><p>Demonstrated technical leadership in cross-functional projects; ability to independently own technical solution design, architecture, and execution in ambiguous 0→1 environments.</p></li><li><p>Strong communication skills to collaborate with engineering, product, data, and business stakeholders</p></li></ul> <p></p> <p><span>Preferred qualifications:</span></p> <ul><li><p><span>Experience building short-form video or content-based recommendation systems, including ranking, retrieval, exploration/exploitation, and diversity modeling.</span></p></li><li><p><span>Deep knowledge of real-time personalization challenges such as cold start, feedback loops, delayed labels, and temporal dynamics.</span></p></li><li><p><span>Experience with experimentation platforms (e.g., A/B testing, bandits, reinforcement learning) to drive continuous optimization of recommendation systems.</span></p></li><li><p><span>Experience designing ML systems on cloud platforms (AWS, GCP, Azure) with distributed compute, streaming data, and scalable online serving.</span></p></li><li><p><span>Familiarity with retrieval models, approximate nearest neighbor search, graph-based recommenders, and large-scale embedding management.</span></p></li><li><p><span>Experience collaborating with product and business stakeholders to define personalization goals, metrics, and KPIs.</span></p></li><li><p><span>Strong mentoring capability to help grow and guide a new ML team; prior experience establishing technical standards, ML development best practices, and team capability building.</span></p></li><li><p><span>Prior experience operating in a fast-paced startup or new product incubation environment.</span></p></li></ul> <p></p> <p><span>Experience with:</span></p> <ul><li><p>8+ years of hands-on experience building and deploying machine learning models in production environments, with at least 2+ years in recommendation systems or personalization.</p></li></ul> <p></p> <p><span>Required Education &nbsp;</span></p> <ul><li><p>Bachelor’s degree in Computer Science, Information Systems, Software, Electrical or Electronics Engineering, or comparable field of study, and/or equivalent work experience</p></li></ul> <p></p> <p>#DISNEYTECH</p> <p><br>The hiring range for this position in Los Angeles, CA is $202,900 - $272,100 per year, in San Francisco, CA $222,200 - $297,900 and in New York &amp; Seattle, WA is $212,600 - $285,100<br/> per year. The base pay actually offe

<p><strong>About the Role</strong><br><br>The Shopping Ranking Team mission is enabling eaters to effortlessly make shopping decisions and find what they need. We pursue this mission via an ML-driven algorithmic approach, applying state-of-the-art Machine Learning (ML), Optimization techniques to learn from massive datasets Uber has, and build a scalable and reliable shopping intelligence ranking and recommendation systems. We are actively seeking individuals who excel in problem-solving and critical thinking, are proficient in coding, with proven track records of learning and growth, and have a deep interest in ML model, feature and infrastructure development. Candidates will have the opportunity to work across various lines, from infrastructure development to ML model development, productionalization, offering a diverse and enriching experience. Join us in our pursuit of excellence as we are building the next generation of shopping ranking and recommendation systems.<br><br><strong>What the Candidate Will Need / Bonus Points</strong><br><br>---- What the Candidate Will Do ----<br><br><ul><li>Design and build Machine Learning models in Ranking and Recommendation domain.</li><li>Productionize and deploy these models for real-world application.</li><li>Review code and designs of teammates, providing constructive feedback.</li><li>Collaborate with Product and cross-functional teams to brainstorm new solutions and iterate on the product.</li></ul><br>---- Basic Qualifications ----<br><br><ul><li>Bachelor's degree or equivalent in Computer Science, Engineering, Mathematics or related field, with 4+ years of full-time engineering experience.</li><li>2+ years of ML experience and building ML models</li><li>Experience working with multiple multi-functional teams(product, science, product ops etc).</li><li>Expertise in one or more object-oriented programming languages (e.g. Python, Go, Java, C++).</li><li>Experience with big-data architecture, ETL frameworks and platforms, such as HDFS, Hive, MapReduce, Spark, , etc.</li><li>Working knowledge of latest ML technologies, and libraries, such as PyTorch, TensorFlow, Ray, etc.</li><li>Proven track records of being a fast learner and go-getter, with willingness to get out of the comfort zone.</li></ul><br>---- Preferred Qualifications ----<br><br><ul><li>Experience with building ranking and recommendation systems in production, making practical tradeoffs among algorithm sophistication, compute complexity, maintainability, and extensibility in production environments.</li><li>Experience with taking on vague business problems, translating them into ML + Optimization formulation, identifying the right features, model structure and optimization constraints, and delivering business impact.</li><li>Experience with design and architecture of ML systems and workflows.</li><li>Experience owning and delivering a technically challenging, multi-quarter project end to end.</li></ul><br>For New York, NY-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.<br><br>For San Francisco, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.<br><br>For Sunnyvale, CA-based roles: The base salary range for this role is USD$198,000 per year - USD$220,000 per year.<br><br>For all US locations, you will be eligible to participate in Uber's bonus program, and may be offered an equity award &amp; other types of comp. You will also be eligible for various benefits. More details can be found at the following link https://www.uber.com/careers/benefits.<br><br>Uber is proud to be an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. If you have a disability or special need that requires accommodation, please let us know by completing this form.<br><br>Offices continue to be central to collaboration and Uber's cultural identity. Unless formally approved to work fully remotely, Uber expects employees to spend at least half of their work time in their assigned office. For certain roles, such as those based at green-light hubs, employees are expected to be in-office for 100% of their time. Please speak with your recruiter to better understand in-office expectations for this role.</p>

<h2>About Direct To Consumer</h2> <p>Disney’s Direct to Consumer group seeks a talented and creative data engineer/scientist. In order to build and deploy Data Science models at the scale and velocity required for Personalization, Forecasting, Marketing, Content/User Segmentation, and other services at the user and session level at various timescales, our Data Scientists work with ML Engineers to build data ingestion pipelines and robust prediction modeling infrastructure. This is a high impact role where your work informs decisions affecting millions of consumers, with a direct tie to The Walt Disney Company’s revenue. It spans a broad range of subject areas, including analyzing fan engagement and marketing, personalization, predicting future consumer behavior, and optimizing content. We are a tight data-driven team with big goals seeking people who solve the toughest challenges at scale, using and building distributed systems in a fast-paced collaborative team environment. </p> <p></p> <h2>In this position, you will: </h2> <ul><li>Work together with data scientists to build and deploy models </li><li>Leverage your in-depth knowledge of data science models to optimize them </li><li>Develop the infrastructure required to build machine learning models at scale </li><li>Build products/algorithms that will be used by millions of D+, E+ and Hulu subscribers </li><li>Build and maintain self-service tools for Data Science </li><li>Present your research and insights clearly and concisely to all levels of the company </li><li>Collaborate across the org including Science, Engineering, Product and Operations </li></ul> <p></p> <h2>Basic Qualifications </h2> <ul><li>5+ years experience driving business impact with data engineering </li><li>Degree in Computer Science, Mathematics, or related field </li><li>Expertise with common languages Python, SQL and a proven ability to learn new programming languages </li><li>Expertise with Spark, Snowflake and related big data tools </li><li>Experience deploying and optimizing machine learning flows, models, and frameworks (Scikit-Learn, Pytorch, Keras, OpenAI etc.) in AWS platforms at scale </li><li>Experience working with data subject to privacy, governance and legal policies </li></ul> <p></p> <h2>Preferred Qualifications </h2> <ul><li>8+ years experience driving business impact with data engineering </li><li>Experience with Scala, Java, Apache, Kafka, Hadoop, Redis or SQS </li><li>Proficiency with Databricks and Snowflake in complex AWS environments </li><li>Proficiency with DevOps and CI/CD using tech like Docker, Kubernetes, Jenkins and Github actions </li><li>Exerience with Agile environment</li><li>Experience with data science feature marts </li><li>Understanding of recent developments in LLMs, vector embeddings, and transformers </li><li>Strong data anaysis and visualizations skills to convey information and results clearly </li><li>Experience applying math and statistical methods to data, including probabilistic thinking and programming </li><li>Exceptional interpersonal and communication skills. </li><li>Impactful presentation skills in front of a large and diverse audience. </li></ul> <p></p> <h2>Additional Information</h2> <p>#DISNEYTECH</p> <p></p> <p><br>The hiring range for this position in Santa Monica, CA is $138,900 to $186,200 per year. The hiring range in Seattle, WA and New York City, NY is $145,400 - $195,000, and the range in San Francisco, CA is $152,100 - 203,900. The base pay actually offered will take into account internal equity and also may vary depending on the candidate's geographic region, job-related knowledge, skills, and experience among other factors. A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial and/or other benefits, dependent on the level and position offered.</p>

The Sponsored Products and Brands team at Amazon Ads is reimagining the advertising landscape through industry-leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond.<br/><br/>We reinvent advertising experiences by bridging human creativity with artificial intelligence—transforming every aspect of the advertising lifecycle, from ad creation and optimization to performance analysis and customer insights. We develop responsible, intelligent AI technologies that balance advertiser needs, enhance the shopping experience, and strengthen the marketplace.<br/><br/>Our systems and algorithms operate on one of the world's largest product catalogs, matching shoppers with advertised products with a high relevance bar and strict latency constraints. We work hand-in-hand with Machine Learning and NLP research scientists to deliver highly relevant ads and continuously improve the customer search and detail page experiences.<br/><br/>We're looking for curious, customer-obsessed engineers with a startup mentality—those who seek a disruptive yet clear mission, have an owner's mindset, and are relentlessly focused on delivering amazing products.<br/><br/>Key job responsibilities<br/>- Drive the technical direction of ML offerings across the Sponsored Products organization<br/>- Design, code, troubleshoot, and support scalable ML pipelines and online serving systems<br/>- Work closely with applied scientists to optimize ML model performance and infrastructure<br/>- Implement end-to-end solutions—what you create is what you own<br/><br/>About the team<br/>The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

Build the machine learning models and infrastructure that deliver relevant, personalized ad experiences across non-Search surfaces on Amazon.com — from product detail pages to the homepage — incorporating deep product and shopper understanding to identify the most useful advertisements for hundreds of millions of customers. You'll drive ML innovation at Amazon Ads scale with direct, measurable customer impact.<br/><br/>The Off-Search Sourcing and Relevance team within Sponsored Products develops state-of-the-art ML models, large-scale data pipelines, and low-latency ad serving systems that power discovery beyond Search. We conduct rapid A/B experimentation to ensure we surface the most relevant ads to downstream systems for click-through prediction and auction. You'll work with Product Managers and Scientists to solve complex problems at scale — building ML infrastructure, driving product initiatives, and launching solutions in one of Amazon's fastest growing and most profitable businesses.<br/><br/>Key job responsibilities<br/>- Design, build, and operate ML infrastructure and data processing pipelines that power ad relevance and sourcing at massive scale<br/>- Develop and optimize machine learning models incorporating deep product and shopper understanding to identify relevant advertisements across non-Search surfaces<br/>- Architect and build ad serving systems that solve real-world customer use cases with high volume, low latency, and strict availability requirements<br/>- Integrate ML solutions with large-scale distributed systems for click-through prediction and ad auction<br/>- Measure impact and customer response through rapid A/B experimentation, iterating to improve relevance and performance<br/>- Collaborate with Product Managers and Scientists to translate complex business problems into scalable ML-driven solutions<br/><br/>About the team<br/>The Sponsored Products and Brands team at Amazon Ads is re-imagining the advertising landscape through industry leading generative AI technologies, revolutionizing how millions of customers discover products and engage with brands across Amazon.com and beyond. We are at the forefront of re-inventing advertising experiences, bridging human creativity with artificial intelligence to transform every aspect of the advertising lifecycle from ad creation and optimization to performance analysis and customer insights. We are a passionate group of innovators dedicated to developing responsible and intelligent AI technologies that balance the needs of advertisers, enhance the shopping experience, and strengthen the marketplace. If you're energized by solving complex challenges and pushing the boundaries of what's possible with AI, join us in shaping the future of advertising.

Machine Learning Engineer at Root access. Apply via Ashby.