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Senior ML Operations (MLOps) Engineer

Eight Sleep · Anywhere

Publicada em 04/10/2026

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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. 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. 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. This isn't a 9-to-5. Our team is deeply committed, often putting in the extra effort, not because we're told to, but because we're invested in building something category-defining. The Role Join our team as a Sr MLOps Engineer to help us bring current and next generations of Pod ML models to life. You'll be a part of a small team designing and implementing solutions with high levels of autonomy to bring our members better sleep. Your work will go directly to our fleet of existing Pods with low friction and direct impact to the business. We are a fast moving and fast growing company, and we embrace individuals with a growth mindset and strong desire to help us achieve our mission: Improving people's lives through optimal sleep. How you’ll contribute Pioneer Cutting-Edge Technology: Introduce and implement cutting-edge ML technologies, integrating them into our products and processes to enable the future of health monitoring End-to-End Ownership: Own design and operation of robust ML inf
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