Machine Learning Engineer (Foundation Models & Personalization)
- Compensation
- Not disclosed
- Location
- San Francisco
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Full time
- Experience
- 2+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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.
Full 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.
High Standards. No Apologies
We operate with intensity because our mission demands it. At Eight Sleep, we bring the same mindset as the world’s top performers: focused, relentless, and always pushing to be in the top 1% of our craft. Think Kobe Bryant’s mamba mentality, applied to bold ideas, next-gen tech, and flawless execution. This isn’t a 9-to-5. We’re a team that puts in the extra effort, not because it’s required, but because we care about the impact of our work. We’re here to build fast, push limits, and deliver without compromise. If you thrive under pressure and want to do the most meaningful work of your career, you’ll feel right at home. If you’re looking for something easier – this isn’t it.
The role
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.
This role is ideal for someone who loves end-to-end ownership: from problem framing → prototyping → offline evaluation → online experimentation → production deployment → iteration.
How you’ll contribute
- 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.