Machine Learning Engineer Job at Eight Sleep (San Francisco)

Machine Learning Engineer

Eight Sleep

Location:
United States , San Francisco

Category:
IT - Software Development

Contract Type:
Not provided

Salary:

Not provided

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Job Description:

Join the Sleep Fitness Movement. At Eight Sleep, we’re on a mission to fuel human potential through optimal sleep. As the world’s first sleep fitness company, we’re redefining what it means to be well-rested and building the most advanced hardware, software, and AI technology to make it possible. Our products power peak mental, physical, and emotional performance by transforming every night of sleep into a personalized, data-driven recovery experience. We are trusted by high performers, professional athletes, and health-conscious consumers in over 30 countries worldwide. Recognized as one of Fast Company's Most Innovative Companies in 2019, 2022, and 2023, and twice named to TIME's “Best Inventions of the Year.” We operate like a high-performance team: fast, focused, and motivated by impact. We don’t just ship; we iterate, refine, and obsess over the details that help our members sleep better and wake up stronger. Every role at Eight Sleep is a chance to create cutting-edge technology, collaborate with world-class talent, and help shape a future where sleep isn’t passive - it’s a powerful tool for living better. If you’re tired of the ordinary and driven to build at the edge of what’s possible, this is your moment. Join us and lead the movement that’s transforming how the world sleeps and what we’re all capable of when we wake up.

Job Responsibility:

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

Nice to have:

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

What we offer:

Equity participation
Periodic equity refreshments based on performance
Every employee receives a Pod (product)

Additional Information:

Job Posted:
February 21, 2026

Employment Type:

Fulltime

Work Type:

On-site work

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