Machine Learning Engineer

Summary

Join Weedmaps as a Machine Learning Engineer and build cutting-edge AI and machine learning systems for our marketplace and e-commerce platform. Collaborate with cross-functional teams to develop and deploy ML solutions addressing unique challenges in the cannabis industry, such as product matching and personalized recommendations. You will create production-ready Python-based ML models, refine machine learning pipelines, and implement automated evaluation pipelines. Build and maintain scalable ML infrastructure using AWS SageMaker and other services. Design and implement A/B tests to evaluate ML system performance and build API-based microservices. This role requires a Bachelor's degree in a related field, 2+ years of experience building and deploying ML models, and strong Python programming skills. The ideal candidate will have experience with MLOps, various ML algorithms and frameworks, and cloud computing platforms.

Requirements

• Bachelor's degree in Computer Science, Data Science, or related quantitative field
• 2+ years of experience building and deploying machine learning models in production environments
• 4+ Years of relevant experience in Machine Learning, Data Science, Data/Software Engineering
• Strong programming skills in Python 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

• Develop production-ready Python-based ML models with a focus on advanced NLP, similarity metrics, and product matching and recommendations
• Create and refine machine learning pipelines that can handle the unique challenges of our product data, including inconsistent naming and categorization
• Develop comprehensive evaluation frameworks including evals and metrics to benchmark ML model performance in real-world scenarios
• Implement automated evaluation pipelines to continuously monitor model performance in production
• Build 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
• E-commerce or marketplace business experience 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