Machine Learning Engineer (Foundation Models & Personalization)

• We’re looking for a Machine Learning Engineer to build and ship consumer-facing AI systems that power personalization, coaching, and next-generation “sleep intelligence.”
• You’ll work across data, modeling, product, and engineering to translate research into reliable, measurable improvements for members
• Build and deploy ML models that improve sleep experiences through personalization, prediction, and behavior understanding (e.g., readiness forecasting, event detection, individualized recommendations)
• Apply and adapt foundation-model capabilities to real product workflows (LLM + tools/RAG, multimodal modeling, policy learning), including MCP-style integrations where helpful
• Develop user behavior models that connect longitudinal signals (sleep, environment, routines) to actionable interventions - grounded in robust experimentation and measurement
• Design evaluation strategies (offline metrics, slice-based analysis, calibration, reliability, fairness) and partner with Product to run high-quality online experiments
• Productionize models: scalable training/inference pipelines, model monitoring, drift detection, alerting, and continuous improvement loops
• Collaborate with cross-functional partners (Product, Mobile, Backend, Clinical) to scope requirements and ship high-impact features

Benefits

• Flexible PTO
• Paid parental leave
• Comprehensive medical insurance
• Your own Pod
• Pet-friendly offices
• Equity participation & periodic refreshes
• Fast career growth & salary progression
• Weekly fitness classes in our offices
• Work directly with founders and executives
• 5 in-person offices in NYC, Boston, SF, Shenzhen and Milan- This role is ideal for someone who loves end-to-end ownership: from problem framing → prototyping → offline evaluation → online experimentation → production deployment → iteration
• Fluency with data tooling (SQL, distributed compute such as Spark/Ray, and cloud storage/compute)
• Strong product sense: you can translate ambiguous goals into measurable outcomes and iterate quickly with stakeholders
• Hands-on experience with large-scale model training and evaluation (PyTorch/TensorFlow/JAX), and strong Python engineering practices
• Experience with personalization systems (ranking/recommendations, segmentation, lifecycle modeling, propensity/behavior modeling, causal/experiment-aware thinking)
• Strong ML fundamentals across supervised learning, sequence/time-series modeling, and modern deep learning
• 2+ years building ML systems in production, ideally for consumer-facing products
• Experience applying LLMs/foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals)
• Experience with privacy-preserving approaches (on-device/federated learning, differential privacy, data minimization)
• Experience with multimodal data (sensor signals + context) and/or health/biometrics data
• Experience designing experimentation frameworks or causal inference approaches for personalization