Senior MLOps Engineer — Scalable Sleep AI

Eight Sleep, Inc.; Alexandria (VA); A distancia; USD 180.000 - 230.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Health insurance

Vision insurance

Dental insurance

Equity

Flexible PTO

Parental leave

Commuter benefits

Descripción de la vacante

Eight Sleep seeks a Sr MLOps Engineer to bring current and next generations of Pod ML models to life. You will join a small, autonomous team designing and implementing ML infrastructure that directly impacts our sleep product lines. Work with cross-functional teams to ensure reliable, scalable ML inference across devices.

The role emphasizes ownership, fast iteration, and collaboration with R&D, firmware, data, and backend units. A growth mindset and efficient remote communication are essential.

Formación

• 5+ years of software engineering experience focusing on ML infrastructure, distributed data processing, or large-scale systems.
• Hands-on experience with ML workflow orchestration and CI/CD pipelines for model deployment.
• Proven ability to ship ML models to production with telemetry, monitoring, and feedback loops.

Responsabilidades

• Design and operate robust ML infrastructure and deployment pipelines.
• Build scalable data, model, and deployment workflows for production.
• Collaborate with R&D, firmware, data, and backend teams to ensure reliable ML inference.
• Optimize compute, storage, and deployment for cost, latency, and scalability.
• Develop tooling and frameworks to streamline data processing, experimentation, and deployment.
• Communicate effectively in a remote work environment.

Conocimientos

Python

ML infra

Distributed systems

Cloud AWS

CI/CD

PyTorch

TensorFlow

Herramientas

Docker

Kubernetes

Terraform

Descripción del empleo

Eight Sleep seeks a Sr MLOps Engineer to bring current and next generations of Pod ML models to life. You will join a small, autonomous team designing and implementing ML infrastructure that directly impacts our sleep product lines. Work with cross-functional teams to ensure reliable, scalable ML inference across devices.

The role emphasizes ownership, fast iteration, and collaboration with R&D, firmware, data, and backend units. A growth mindset and efficient remote communication are essential.