Senior Machine Learning Engineer Tech Lead

Summary

Join Weedmaps as a Senior Machine Learning Engineer Tech Lead and become a key technical leader, contributing to the Data organization. Lead a team in building and deploying sophisticated AI and machine learning systems for our marketplace and e-commerce platform. Provide technical leadership, strategic direction, and tactical execution. Collaborate with cross-functional teams, including Product, Engineering, Data and Analytics, Legal, and Finance. This is a technical leadership role with a dotted-line reporting structure, focusing on technical execution and not direct people management. The role involves setting technical goals, ensuring quality, establishing engineering standards, and collaborating with stakeholders to translate business needs into technical solutions. You will also be responsible for developing and implementing ML model effectiveness frameworks and optimizing model deployment pipelines.

Requirements

• 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)

Responsibilities

• 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

Preferred Qualifications

• 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 re-rankers that optimize specific metrics (e.g. precision)
• Successfully built and deployed ML systems that solved real business problems in e-commerce or marketplace environments
• Successful deployment of ML systems in e-commerce or marketplace business environments preferred
• Regulated industry experience - nice to have

Benefits

• Physical Health benefits: Medical, Dental & Vision
• Employee - employer paid premium 100%
• Company contribution to a HSA when electing the High Deductible Health Plan
• For plans that offer coverage to your dependents, you pay a small contribution
• Mental Health benefits
• Free access to CALM app for employees and dependents
• Employee Training
• Mental Health seminars and Q&A sessions
• Basic Life & AD&D - employer paid 1x salary up to $250,000
• 401(k) Retirement Plan (with employer match contribution)
• Generous PTO, Paid Sick Leave, and Company Holidays
• Supplemental, voluntary benefits
• Student Loan Repayment/529 Education Savings - including a company contribution
• FSA (Medical, Dependent, Transit and Parking)
• Voluntary Life and AD&D Insurance
• Critical Illness Insurance
• Accident Insurance
• Short- and Long-term Disability Insurance
• Pet Insurance
• Family planning/fertility
• Identity theft protection
• Legal access to a network of attorneys
• Paid parental leave