AI/ML Research Internship

Eight Sleep
San FranciscoPosted 21 February 2026

Job Description

Join the Sleep Fitness MovementAt 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. Our team is deeply committed, often putting in 60+ hours a week –not because we’re told to, but because we’re invested. 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 RoleEight Sleep is looking for Machine Learning research interns to work on AI/ML problems in the sleep fitness and personal health space. You’ll work closely with a cross-functional team to scope problems, build end-to-end prototypes, validate with data, and iterate toward something we can ship. Along the way, you’ll receive hands-on mentorship, sharpen your technical toolkit, and present your results to leadership. Where appropriate, you may also develop your work into a publication.We’re seeking interns who care about outcomes, think in systems, and make data-driven decisions. If you’re excited to learn fast, collaborate deeply, and deliver meaningful improvements for users, we’d love to work with you.What You'll Help BuildExample topics include:Multimodal Activity Understanding: Build and fine-tune a vision- and physiology- foundation model that detects daily activities (workouts, meals, naps) from wearables, Pod signals, and phone context to explain sleep trends.Next-Gen Time-Series Forecasting for Sleep: Push state of the art on multivariate forecasting to predict sleep stages, heart rate, HRV, and recovery days ahead - robust to missing data and lifestyle shifts.Lifestyle-to-Sleep Simulation (“Digital Twin”): Create a high-fidelity simulator that models how choices (caffeine, exercise timing, alcohol, travel) ripple into tonight’s sleep and tomorrow’s readiness.Adaptive Pod Thermoregulation: Learn personalized cooling/heating policies that react to micro-events (restlessness, awakenings) in real time - balancing comfort, sleep continuity, and energy.Privacy-Preserving Personalization: Train better models without centralizing raw data: federated fine-tuning with differential privacy for sleep staging, anomaly detection, and forecasting.What You'll Need to SucceedMinimum QualificationsWorking toward an undergraduate, graduate or doctoral degree in computer science, engineering, data science, appli ... (truncated, view full listing at source)
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