Senior Machine Learning Engineer Tech Lead

Senior Machine Learning Engineer Tech Lead (Hybrid)

Overview:

The Senior Machine Learning Engineer Tech Lead at Weedmaps will be a key technical leader and contributor within our Data organization, leading the technical execution for a multi-disciplinary team of 3-5 engineers. In this role you will provide technical leadership, strategic direction, expert guidance, and tactical execution to build and deploy sophisticated AI and machine learning systems that power our marketplace and e-commerce platform. The ideal candidate is an experienced ML practitioner with strong software engineering skills and experience guiding teams to deliver end-to-end production ML systems that deliver measurable business impact. In addition to setting the direction for your cross-functional team, you will also collaborate extensively with teams and leaders across the business, including Product to understand user needs and translate them into ML solutions; Engineering to integrate ML systems into our broader ecosystem; Data and Analytics to leverage insights and coordinate on data strategies; as well as legal and finance to ensure our ML systems are compliant.

Note that this is a technical leadership role with a dotted-line reporting structure, not a true people leader role.

The impact you'll make:

• Lead the technical execution of a multi-disciplinary team including application engineers, ML engineers, data engineers, and other specialists, including setting clear technical goals and timelines
• Own delivery quality and establish engineering and architectural standards and design patterns for ML services
• Coordinate cross-team dependencies and ensure seamless integration of ML systems
• Develop and implement frameworks for measuring ML model effectiveness, including comprehensive evaluation metrics and automated evaluation pipelines
• Partner with stakeholders to translate business requirements into technical roadmaps and resource allocation decisions
• Oversee the development of production-ready Python-based ML models with a focus on advanced NLP, similarity metrics, and product matching and recommendations
• Provide expert guidance on architecture, tooling, and implementation of ML pipelines and workflows
• Architect and maintain scalable ML infrastructure using a mix of managed services (eg AWS SageMaker) and custom services (such as function as a service apps on Kubernetes)
• Implement best practices for model serving, versioning, and monitoring in production environments
• Optimize model deployment pipelines for reliability, performance, and cost-efficiency
• Design, implement, and analyze A/B (or MAB) tests to evaluate ML system performance in production systems (e.g. with Optimizely or similar tools), ensuring that ML systems achieve business objectives
• Design and build API-based microservices that integrate ML functionality into our broader engineering ecosystem, ideally creating reusable ML components that can be leveraged across multiple product lines

What you've accomplished:

• Bachelor's degree in Computer Science, Data Science, or related quantitative field
• 2+ years of experience leading technical teams in a Tech Lead capacity, preferably in ML/AI applications
• 4+ years of experience building and deploying ML/AI models in production environments
• 6+ Years of relevant experience in Machine Learning, Data Science, Data/Software Engineering
• Demonstrated ability to establish and maintain high engineering standards and quality
• Expert Python skills and experience with modern LLM endpoints
• Experience with MLOps practices for model monitoring, maintenance, and lifecycle management
• Demonstrated expertise in machine learning algorithms and frameworks (e.g. TensorFlow, PyTorch, or scikit-learn) as well as modern LLM systems (Anthropic, OpenAI) with a proven track record of deploying models to production
• Proficiency in software engineering best practices, including version control, code review, testing, and documentation
• Strong understanding of data engineering principles and experience with data preprocessing, feature engineering, and data quality assurance
• History of effective collaboration with cross-functional teams to deliver ML solutions that drive measurable business results
• Experience communicating complex ML concepts to both technical and non-technical stakeholders
• Experience with cloud computing platforms, preferably AWS (particularly SageMaker and Bedrock)

Bonus points:

• Experience using AI endpoints such as Claude or ChatGPT for embeddings and more advanced AI pipeline use cases such as hybrid ranking systems leveraging RAG with AI-based r