Machine Learning Engineer

Machine Learning Engineer (Hybrid - Onsite 2 days a week)

Overview:

The Machine Learning Engineer at Weedmaps will be a key technical contributor within our Data organization. In this role you will build and deploy sophisticated AI and machine learning systems that power our marketplace and e-commerce platform. The ideal candidate is a hands-on ML practitioner with strong software engineering fundamentals who can build end-to-end systems that deliver measurable business impact. You will collaborate extensively with cross-functional teams, 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 stakeholders across the business to ensure ML initiatives are aligned with company objectives.

As part of our Data organization, you will develop and deploy machine learning solutions that address unique challenges in our marketplace, including product matching, personalized recommendations that comply with complex regulatory requirements, and data-driven optimizations across our marketplace. The explosive growth of the cannabis industry requires increasingly sophisticated ML solutions that can scale with our business.

The impact you'll make:

• 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

What you've accomplished:

• 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)

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 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

The base pay range for this position is $181,875.00 - $200,645.00 per year

2025 Benefits for Full Time, Regular Employees:

• 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
• Generous PTO and company holidays