Senior Machine Learning Engineer (Growth)

• The Growth Machine Learning Engineering team partners closely with Data Science to take models from experimentation into reliable production
• We own the underlying ML data infrastructure: standardizing shared feature definitions, eliminating data leakage in training pipelines, automating scheduled retraining, and establishing safeguards so faulty batch scoring runs never affect live promotions
• By managing data integrity, deployment pipelines, and production monitoring, we provide the dedicated engineering support needed to keep model-driven growth initiatives dependable and scalable
• Own the feature layer between the Growth DB and the models. Work with data engineering on schema design for ML features, and with data science to turn their feature lists into versioned feature definitions that training and scoring both read from
• Build point-in-time correct training data, so a retrain sees each day as it was and does not leak future data. Data science has already named this as a gap in today’s pipelines
• Take models data science has trained and put them into production: validate against the offline results, wire them into batch scoring, and add input and output checks that stop a bad run before promos go out
• Lead training automation: scheduled retrains, a model registry, and validation before a new model version is promoted
• Monitor models in production: feature drift, score distribution shifts, and model versus random arm results
• Be data science’s day-to-day engineering counterpart, so feature and modeling questions do not queue behind the tech lead

Benefits

• Medical, dental, and vision insurance for employees and dependents
• Employee assistance programs focused on mental health
• Personalized workplace adjustments for diverse needs and abilities, including physical, mental, and neurodivergent considerations
• Access to mental health apps
• Onsite wellness centers
• Support programs including menopause benefit
• Medical advocacy program for transgender employees
• Second medical opinion for you and your loved ones
• Counseling services
• Competitive compensation
• Regular bonus and equity performance grant opportunities
• Generous 401(k) and regional retirement plans
• 1-on-1 financial coaching
• Annual cross-company compensation review and pay equity analysis
• Fertility and growing family assistance
• Parental leave and baby bonding leave
• Survivor income benefit
• Backup childcare
• Elder care and support
• Caregiver leave
• Paid time off, including vacation, bereavement, sick leave, parental leave, disability, and holidays
• Jury duty leave
• Military leave
• Hybrid work model with remote work opportunities also available
• Educational reimbursement
• Peer learning and coaching platform
• Donation matching programs
• Volunteer hours
• Employee resource groups
• Internal community groups and local culture clubs
• Inspiring spaces to work, recharge, and collaborate with fellow Waymonauts
• On-site meals and snacks
• Fitness centers, massage programs, and ergonomic support
• On-demand fitness, wellbeing, and cooking classes
• Commuter benefits- Strong SQL and Python, plus large-scale data processing (Flume, Beam, Spark or similar)
• Productionized and owned the lifecycle of an existing offline model
• Experience at roughly building production ML systems: feature pipelines, training data generation, batch inference
• Worked with data scientists and can turn a loose feature request into a spec
• Designed feature schemas or worked on a feature store, and understands point-in-time correctness and train/serve skew from having fixed them
• Background in uplift modeling, causal inference or experimentation (A/B tests, holdouts), enough to understand what the models need
• Experience in ads, marketing, recommendations, pricing or promotions
• Familiarity with feature stores, workflow orchestration (Airflow or similar) and distributed SQL databases