Machine Learning Engineer (Foundation Models & Personalization)

About the role

We're seeking a Machine Learning Engineer to build and ship consumer-facing AI systems that enhance sleep experiences through personalization, prediction, and behavior understanding.

Responsibilities

• 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.

Requirements

• 2+ years building ML systems in production, ideally for consumer-facing products.
• Strong ML fundamentals across supervised learning, sequence/time-series modeling, and modern deep learning.
• 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).
• 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.

Bonus Points

• Experience applying LLMs/foundation models to product features (tool use, retrieval, structured outputs, guardrails, evals).
• Experience with multimodal data (sensor signals + context) and/or health/biometrics data.
• Experience with privacy-preserving approaches (on-device/federated learning, differential privacy, data minimization).
• Experience designing experimentation frameworks or causal inference approaches for personalization.