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Senior ML Operations (MLOps) Engineer

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Remote from
🌐 Anywhere
Salary
Undisclosed
Employment
Full Time
Experience
Senior
Published
Apply before
3 Nov 2026
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188
Application actions
8
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AI Summary

The role, at a glance.

Eight Sleep is hiring a Senior MLOps Engineer to design, operate, and scale the infrastructure that delivers machine-learning models to its connected sleep products. The role owns end-to-end ML data, training, deployment, telemetry, monitoring, and feedback pipelines, with a focus on reliable production inference across a large device fleet. It requires strong Python, distributed-systems, cloud-native AWS, CI/CD, and ML workflow orchestration experience. The engineer will collaborate closely with R&D, firmware, data, and backend teams in a high-autonomy, fast-moving remote environment.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

5/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

5/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThis is a senior, production-critical MLOps role spanning distributed ML infrastructure, cloud optimization, deployment reliability, and device-fleet observability. The candidate must make independent technical decisions while coordinating across several engineering disciplines in a high-intensity environment.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job medianMarket rate
$180,000
US market range$150k–$220k
AI insightNo direct salary was disclosed, so these are estimated US-market annual base-salary figures for a senior MLOps engineer. Actual compensation may vary materially by candidate location, cloud/ML systems depth, and the value of equity or other benefits.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
Describe an ML deployment platform you built or significantly improved. What were its core components and outcomes?

I would describe the data validation, model registry, automated testing, deployment orchestration, rollout controls, and monitoring components. I would quantify improvements such as faster release cycles, lower model-serving latency, improved reliability, or reduced operational effort.

How would you safely deploy a new inference model to a large fleet of connected devices?

I would version the model and runtime, validate compatibility, test through offline and hardware-in-the-loop stages, then use a staged rollout or canary cohort. I would monitor device health, latency, prediction quality, resource use, and error rates, with clear rollback criteria and an immediate rollback path.

How do you design observability for production ML systems?

I track conventional service metrics such as availability, latency, throughput, failures, and cloud cost alongside ML-specific signals such as feature quality, drift, prediction distributions, model-version performance, and feedback-loop outcomes. Dashboards and alerts should be tied to actionable thresholds and model ownership.

Give an example of optimizing ML infrastructure cost without compromising reliability or performance.

A strong answer would explain measuring the main cost drivers first, then selecting actions such as right-sizing compute, autoscaling, batching, caching, storage lifecycle policies, or moving suitable workloads to asynchronous processing. The result should be validated against service-level objectives, inference latency, and model-quality metrics.

How would you collaborate with firmware, backend, data, and R&D teams when an ML feature is failing in production?

I would establish a shared incident scope, correlate telemetry across the device, backend, data, and model layers, and assign clear owners for investigation. After mitigation, I would lead a blameless review and convert findings into durable safeguards such as improved contracts, tests, monitoring, documentation, or rollout controls.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

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.

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 infrastructure – building scalable data, model, and deployment pipelines that ensure reliable delivery of models to production.

  • Cross-functional Collaboration Partner with R&D, firmware, data, and backend teams to ensure ML inference operates reliably and scales to Pods everywhere.

  • Optimize for Performance: Drive cost-effective, scalable, and high-performance ML systems by optimizing compute, storage, and deployment resources across training and inference

  • Enhance Tooling and Platforms: Develop tooling, micro services, and frameworks to streamline data processing, experimentation, and deployment

  • Effective Remote Communication: Thrive in a remote work environment, ensuring clear and direct communication.

What you need to succeed

  • Proven Expertise: 5+ years of software engineering experience with a focus on ML infrastructure, distributed systems, or large-scale data processing in Python (e.g., PyTorch, TensorFlow, or similar).

  • ML Operations Mastery: Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment.

  • Scalable Deployment Experience: Demonstrated success shipping ML models to production at scale, handling telemetry, monitoring, and feedback loops across large device fleets or user populations.

  • Cloud-Native Expertise: Strong experience with AWS (Lambda, ECS, DynamoDB, CloudWatch) or equivalent cloud platforms for serving and monitoring ML systems.

  • Adaptive Problem Solver: A fast-paced, collaborative, and iterative approach to tackling complex problems.

What sets you apart:

  • Expertise in real-time ML workflows and streaming systems (e.g., Kinesis, Kafka, Flink).

  • Demonstrated expertise in optimizing ML infrastructure for efficiency, latency, and cloud cost at scale.

  • Understanding of secure ML operations, privacy practices, and compliance considerations, particularly for health-related or IoT data.

  • Familiarity with health, wellness, or IoT domains, especially wearables or medical-grade devices.

Why join Eight Sleep?

Innovation in a culture of excellence

Join us in a workplace where innovation isn’t just encouraged – it’s a standard. Our flagship product, the Pod, is a testament to our culture of excellence, beloved by hundreds of thousands of customers worldwide. At Eight Sleep, you will be part of a team that continuously pushes the boundaries of technology in sleep fitness.

Immediate responsibility and accelerated career growth

From your first day, you’ll take on substantial responsibilities that have a direct impact on our core business and product success. We are a small team that empowers you to own your projects and see the tangible effects of your efforts, enhancing both your professional growth and our company’s trajectory. Your path will be challenging but rewarding, perfect for those who thrive in fast-paced environments aiming for high standards.

Collaboration with exceptional talent

Work alongside other bright minds like you: at Eight Sleep exceptional intelligence and a passion for breakthroughs are the norms. Our team members are not only experts in their fields but also avid innovators who thrive in our dynamic, fast-paced environment.

Equitable compensation and continuous equity investment

We extend equity participation to every full-time team member, recognizing and rewarding your direct contributions to our success. This includes periodic equity refreshments based on performance, ensuring that as Eight Sleep grows and succeeds, so do you – perfectly aligning your achievements with the broader triumphs of the company.

Your own Pod – and other great benefits

  • Every Eight Sleep employee receives the very product that defines our mission: a Pod of their own. If you join us you’ll get your own Pod, along with*:

  • Full access to health, vision, and dental insurance for you and your dependents

  • Supplemental life insurance

  • Flexible PTO

  • Commuter benefits to ease your daily commute

  • Paid parental leave

*List of benefits may vary depending on your location

At Eight Sleep we continually celebrate the diverse community different individuals cultivate. As an equal opportunity employer, we stay true to our values by ensuring everyone feels they can flourish and grow. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.

Apply now >

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