Machine Learning Engineer Jobs in New York, USA

<h3><strong>The Opportunity</strong></h3> <p>This is a critical and exciting time at Enigma. Our customers consistently tell us that our data products create tremendous value and are deeply aligned with their most important workflows. As demand grows, we have an urgent opportunity to improve both the intelligence of our data and the systems through which customers access it.</p> <p>We are looking for an experienced Senior Machine Learning Engineer to join our Match Team and help shape the next generation of Enigma’s customer-facing data products.</p> <p>In this role, you will combine advanced statistical and machine learning research with the engineering systems required to power fast, relevant, and reliable search experiences at scale. This is a uniquely high-impact role sitting at the intersection of information retrieval, ranking systems, semantic search, distributed systems, and customer data delivery.</p> <h3><strong>The Role</strong></h3> <p>At the core of Enigma’s product is our data, which makes both data science and delivery systems central to what we build.</p> <p>As a Senior ML Engineer on the Match Team, you will lead efforts that improve the relevance, latency, and scalability of our customer-facing data products. You’ll work across the full lifecycle: framing retrieval and ranking problems, developing models and experimentation strategies, evaluating results using real-world signals, and implementing high-throughput search and retrieval systems.</p> <p>This role is ideal for someone who is excited by both hard ranking/search problems and the systems challenges of turning those solutions into low-latency, production-grade retrieval systems.</p> <h3><strong>What You'll Do</strong></h3> <ul> <li>Develop innovative solutions to complex problems in information retrieval, ranking, semantic search, query understanding, and recommendation systems</li> <li>Build and optimize low-latency, high-throughput search APIs, indexing pipelines, and retrieval systems using Python, Typesense, and AWS</li> <li>Evaluate and evolve our search technology stack, driving technical design decisions across indexing strategies, retrieval architecture, and system performance tradeoffs</li> <li>Lead end-to-end work from research design through experimentation, productionization, and customer-facing delivery</li> <li>Design evaluation frameworks for measuring relevance, precision/recall, ranking quality, and user engagement signals</li> <li>Improve query understanding via techniques like embedding models, vector search, hybrid retrieval, and query rewriting</li> <li>Detect and investigate anomalies in search performance, ranking behavior, and data freshness, tracing issues to root cause</li> <li>Partner closely with Product,

The Rewards team in Personalization (PZN) is defining the next generation of large-scale personalization at Spotify by pioneering novel Reinforcement Learning (RL) methods for Large Language Models (LLMs). We drive massive impact across all recommendations discovery surfaces by moving beyond simple clicks to model and optimize for true long-term user satisfaction. Our core mission is to bridge discovery with lasting listening habits by developing and deploying sophisticated, mid-term behavioral reward signals—such as retention and habit formation metrics—that directly shape the LLM-powered recommendation experience. We are looking for a Machine Learning Engineer to make impactful changes to our recommendations and discovery algorithms. As an integral part of the squad, you will collaborate with research scientists, data scientists, and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction. 

<div class="content-intro"><p>Biohub is the first large-scale initiative bringing frontier AI models, massive compute, and frontier experimental capabilities under one roof. We're building a general-purpose system to accelerate scientific discovery, integrating frontier AI models, biological foundation models, and lab capabilities, with the ultimate goal of curing disease. Our technology powers scientists around the world, translating AI capabilities into tools that accelerate research everywhere.</p></div><p>Biohub operates one of the largest AI compute clusters dedicated to biology, spanning three frontier research institutes with some of the world's leading biologists. We're not a startup trying to find product-market fit, and we're not a pharma company optimizing a pipeline. We're building frontier AI for fundamental science, as open science, at a scale no one else is doing. This is a unique moment for scientific acceleration. The problems are among the hardest and most impactful problems you can choose to work on, and we move at a pace that meets this moment. </p> <p>Our research spans:</p> <ul> <li>Frontier molecular modeling, from protein language models (e.g., ESM) to structure prediction (e.g., ESMFold) and beyond. </li> <li>Scaled biological foundation models trained on some of the largest GPU clusters dedicated to science</li> <li>Imaging foundation models trained across the world's largest microscopy datasets</li> <li>Reasoning and agentic systems that connect frontier LLMs with biological foundation models</li> <li>Mechanistic interpretability of biological foundation models: extracting new biological knowledge directly from model weights</li> <li>Scientific data at unprecedented scale: AI systems to collect, curate, and learn from some of the richest biological datasets ever assembled</li> </ul> <h2><span style="color: rgb(40, 40, 39); font-family: helvetica, arial, sans-serif;">Join our Team!</span></h2> <p>As an ML Engineer, you'll join some of the strongest infrastructure engineers in AI, building the systems that connect everything together. The infrastructure problems you solve directly determine what science becomes possible. </p> <h2><span style="color: rgb(40, 40, 39); font-family: helvetica, arial, sans-serif;">What You'll Do</span></h2> <ul> <li>Work with high-dimensional scientific data formats and contribute to backend compatibility, format evaluation, and I/O performance benchmarking at petabyte scale.</li> <li>Define and shape the engineering patterns your team and collaborating researchers will build on for years; the abstractions you write today become the foundation others depend on at scale.</li> <li>Wor

<div class="content-intro"><h3> </h3> <h3><strong>Why Charlie Health?</strong></h3> <p>Millions of people across the country are navigating mental health conditions, substance use disorders, and eating disorders, but too often, they’re met with barriers to care. From limited local options and long wait times to treatment that lacks personalization, behavioral healthcare can leave people feeling unseen and unsupported.</p> <p>Charlie Health exists to change that. Our mission is to connect the world to life-saving behavioral health treatment. We deliver personalized, virtual care rooted in connection—between clients and clinicians, care teams, loved ones, and the communities that support them. By focusing on people with complex needs, we’re expanding access to meaningful care and driving better outcomes from the comfort of home.</p> <p>As a rapidly growing organization, we're reaching more communities every day and building a team that’s redefining what behavioral health treatment can look like. If you're ready to use your skills to drive lasting change and help more people access the care they deserve, we’d love to meet you.</p></div><p><strong>About the Role</strong></p> <p>Charlie Health leads the nation in high-acuity virtual behavioral care, having delivered life-saving treatment to more than 100,000 clients nationwide. Our Matching and Outcomes team builds products to measure and improve clinical outcomes, including provider recommendation systems for clients and clinical decision support tools, enabling durable and effective treatment. You'll create predictive models and workflows to match patients with the best-fit therapists and peers, and enable clinicians to tailor care to each client's individual needs. The systems you build will help clinicians condense and synthesize knowledge across our clients and providers into actionable guidance at every moment. If you care about using data-driven insights to improve outcomes, this team is for you.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Design, train, and evaluate machine learning models and AI systems that drive meaningful business impact</li> <li>Lead ambiguous, high-impact initiatives from start to finish, shaping how ML and AI systems improve clinical outcomes across the product </li> <li>Identify opportunities to introduce or improve data-driven algorithms and automated solutions in partnership with clinical, design, data science, and product teams</li> <li>Establish best practices for ML and generative AI model evaluation, retraining, and experimentation to create clinically responsible systems  </li> <li>Utilize ML infrastructure to serve model inferences in real time using live data streams</li> <li>P

<div class="content-intro"><h3> </h3> <h3><strong>Why Charlie Health?</strong></h3> <p>Millions of people across the country are navigating mental health conditions, substance use disorders, and eating disorders, but too often, they’re met with barriers to care. From limited local options and long wait times to treatment that lacks personalization, behavioral healthcare can leave people feeling unseen and unsupported.</p> <p>Charlie Health exists to change that. Our mission is to connect the world to life-saving behavioral health treatment. We deliver personalized, virtual care rooted in connection—between clients and clinicians, care teams, loved ones, and the communities that support them. By focusing on people with complex needs, we’re expanding access to meaningful care and driving better outcomes from the comfort of home.</p> <p>As a rapidly growing organization, we're reaching more communities every day and building a team that’s redefining what behavioral health treatment can look like. If you're ready to use your skills to drive lasting change and help more people access the care they deserve, we’d love to meet you.</p></div><p><strong>About the Role</strong></p> <p>Charlie Health leads the nation in high-acuity virtual behavioral care, having delivered life-saving treatment to more than 100,000 clients nationwide. Our Matching and Outcomes team builds products to measure and improve clinical outcomes, including provider recommendation systems for clients and clinical decision support tools, enabling durable and effective treatment. You'll create predictive models and workflows to match patients with the best-fit therapists and peers, and enable clinicians to tailor care to each client's individual needs. The systems you build will help clinicians condense and synthesize knowledge across our clients and providers into actionable guidance at every moment. If you care about using data-driven insights to improve outcomes, this team is for you.</p> <p><strong>Responsibilities</strong></p> <ul> <li>Design, train, and evaluate machine learning models and AI systems that drive meaningful business impact</li> <li>Identify opportunities to introduce or improve data-driven algorithms and automated solutions in partnership with clinical, design, data science, and product teams</li> <li>Pioneer new approaches to measure and validate outcomes impact through advanced experimentation and causal inference techniques</li> <li>Utilize ML infrastructure to serve model inferences in real time using live data streams</li> <li>Write production code within a backend Python monolith to invoke ML predictions.</li> <li>Foster a culture of collaboration and learning across engineering, product, and design through mento

<div> <p>We design Spotify’s consumer experience - end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.</p> <p>The Content Platform team powers the full lifecycle of content across music, podcasts, audiobooks, and emerging formats at Spotify. We ensure that everything from licensed catalog to user-generated content is trusted, safe, and high quality for millions of listeners worldwide. Our systems are responsible for how content is ingested, understood, enriched, governed, and distributed across the platform. As the scale and diversity of content continues to grow—driven by advances in AI and new creation tools—we’re building intelligent systems that can evaluate, manage, and route content reliably at global scale.</p> <p>We’re seeking a Staff Machine Learning Engineer to build and scale foundational ML systems that power content understanding across Spotify. In this role, you’ll work on systems that generate deep, machine-readable understanding of content across audio, video, text, and images—enabling automation, improving quality, and unlocking new product experiences. This work is central to delivering safe, high-quality, and differentiated experiences for millions of listeners and creators worldwide.</p> </div>

We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. About the Team The Policy & Safety team sits within the Content Platform domain and builds the systems that keep Spotify safe and trustworthy at scale. We own the infrastructure behind content moderation, including detection models, policy enforcement systems, compliance pipelines, and the safety-by-default platform. Our work sits on the critical path of every new content type and product experience—from messaging and comments to collaborative and agentic features. We partner closely with Trust & Safety, Legal, and Public Affairs to ensure that as Spotify evolves, safety is built in from the start—not added later.

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them. You’ll join a team working at the intersection of machine learning, music understanding, and user experience. We focus on generating music sessions powering experiences like systems that power conversational playlist generation to give users more adaptive and intuitive control over what they listen to. This team collaborates closely with product, design, user research, and data science to build personalized, high-impact features used by hundreds of millions of listeners worldwide.

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.  The Personalization team at Spotify makes deciding what to listen to next feel effortless for hundreds of millions of users — from Discover Weekly to our newest AI-powered experiences. We’re now building conversational AI capabilities that let users interact with Spotify in natural language. You’ll join a squad working at the core of this space, shaping how users discover and engage with audio through intelligent, responsive systems.

We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Policy & Safety team sits within Content Platform in the Experience Mission, building the systems that keep Spotify safe, compliant, and trusted by millions of users and creators. This team owns Spotify’s content moderation infrastructure — from detection models to policy enforcement systems and compliance data pipelines. Working at the intersection of machine learning, platform engineering, and regulatory compliance, the team partners closely with Trust & Safety, Legal, and Public Affairs. They’re on the critical path for every new content type and social feature — including messaging, comments, and collaborative experiences — ensuring safety is built in from day one. With a strong focus on “safety by default,” the team is investing in large-scale rearchitecture and ML-driven systems to proactively protect users and empower safer interactions across the platform.

<div> <div>We design Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify.<br><br>The Verbatim squad sits within the Enrichment &amp; Content Intelligence product area and is focused on helping Spotify better understand audio, text, and visual content through machine learning. The team develops technologies that power experiences across Spotify including content skipping, transcription, moderation, and visual understanding. Working at the intersection of large-scale machine learning and product innovation, the squad partners closely with Product, Engineering, and Data Science teams to build intelligent systems that improve how users experience content across the platform.</div> </div>

The Experience team designs Spotify’s consumer experience—end to end, moment to moment, across every screen, platform, and partner integration. Our mission is to make listening feel effortless, personal, and joyful for billions of users around the world. That means turning complexity into clarity across hundreds of touchpoints—from our mobile and desktop apps to the smart speakers, TVs, cars, and integrations where Spotify shows up every day. If it touches a consumer, we shape it. We bring deep insight into human behavior, design, and technology to craft experiences that feel intuitive, expressive, and unmistakably Spotify. The Enrichment & Content Intelligence team sits within Content Platform in the Experience Mission. We build the metadata-resolution and content-enrichment infrastructure that powers how Spotify understands music and video content at global scale. Our systems help answer foundational questions across the platform: which tracks are the same recording, which music videos match which audio tracks, who wrote and performed a song, and how content relationships connect across Spotify’s catalog. Our infrastructure powers products and experiences used by millions of listeners, artists, and creators every day. From recommendations and charts to royalties and artist tooling, the work we do directly shapes how content is understood and surfaced across Spotify. We’re looking for a Senior Machine Learning Engineer to help evolve the machine learning systems behind Recording Groups, Music Video Resolution, SongDNA, and the Music Knowledge Graph. This role sits at the intersection of multimodal machine learning, entity resolution, and production-scale engineering, with opportunities to work across audio, video, and metadata understanding problems at massive scale.

The Music Promotion team is building products that allow creators to promote their work to reach new audiences and create lasting connections with their fans. We’re looking for a Machine Learning Engineer to help us build systems that more accurately understand the performance that promotion can have, giving customers actionable insights for building their promotion strategies, whether it’s a DIY artist or an industry-facing partner. As an ML Engineer, you will help execute on strategies for understanding the factors that play a role in the performance of promoted tracks across the globe. You’ll build data-driven solutions, as well as effective online and offline strategies to efficiently iterate and evaluate model approaches. You’ll have access to a growing list of datasets, features and ML infrastructure to continually experiment and improve the model-based approach.

Mission Statement The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.   About the Team Safe-and-Sound is the centralized Safety team within the AI Foundations Studio in Personalization. We build machine learning systems that help ensure Spotify experiences and recommendations are safe, responsible, and enjoyable across core surfaces like Home, Search, as well as newer generative AI experiences.   We partner closely with Tech Research, Trust & Safety, and Content Platform to develop new approaches in areas like synthetic data, fairness, and responsible AI. Our focus is on building scalable, high-impact systems that support both today’s products and the next generation of AI-driven experiences.

The Personalization team makes deciding what to play next easier and more enjoyable for every listener. From Blend to Discover Weekly, we’re behind some of Spotify’s most-loved features. We built them by understanding the world of music and podcasts better than anyone else. Join us and you’ll keep millions of users listening by making great recommendations to each and every one of them.    We are looking for a Machine Learning Engineer to join the Personalization (PZN) team - an area of hardworking engineers that are passionate about understanding what drives user satisfaction with Spotify - and who make impactful changes to Home recommendation systems to achieve this goal. As an integral part of the squad, you will collaborate with research scientists, data scientists and other engineers across PZN in prototyping and productizing state-of-the-art ML at the intersection of recommendations and long-term user satisfaction.

The Personalization (PZN) team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music, podcasts and audiobooks better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people all over the world use the products we build which include destinations like Home and Search as well as original playlists such as Made For You, Discover Weekly and Daily Mix. Our team’s mission is to bring emerging search and agentic experiences to a mature state: exploring, defining, building, validating and optimizing new ideas. These can include new content types in our Search engine or emerging user interaction patterns.

The Personalization team makes deciding what to play next on Spotify easier and more enjoyable for every listener. We seek to understand the world of music better than anyone else so that we can make great recommendations to every individual and keep the world listening. Every day, hundreds of millions of people use the products we build, including destinations like Home and Search, original playlists like Discover Weekly and Daylist, and new innovations like AI DJ and AI Playlists. The Surfaces Music team is responsible for music recommendations across Spotify's most visible surfaces, including Home and the Now Playing experience. We own music shelf and candidate generation as well as the ranking models that power these experiences. Our models include embedding models for deep catalog discovery, new release recommendations, and a unified transformer-based generative personalization model that is poised to reshape how we deliver personalized experiences across Spotify.

Machine Learning Engineer (LLM / Personalization)