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Eight Sleep Logo Eight Sleep · IT - Software Development

ML Systems Engineer

United States, San Francisco · Job Posted February 21, 2026
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Job Description

We’re hiring an ML Systems Engineer / Data Engineer to build the platform foundations that power our ML lifecycle - from data ingestion and curation to training, evaluation, deployment, and monitoring. This is a highly leveraged role: your work will accelerate every model, every experiment, and every product launch across AI/ML. You’ll partner with ML engineers, data science, firmware, and backend teams to create scalable, reliable systems for time-series and biometrics-grade data.

Job Responsibility

  • Build and operate high-throughput pipelines for sensor and event data (batch + streaming), with strong guarantees on quality, lineage, and reliability
  • Create scalable dataset curation and labeling workflows: sampling, slice definitions, weak supervision, gold-set management, and evaluation set integrity
  • Develop ML platform components: feature pipelines, training orchestration, model registry, reproducible experiment tracking, and automated evaluation
  • Implement monitoring and observability for production ML systems: data drift, performance regression, alerting, and automated failure detection
  • Standardize schemas and interfaces across studies and product telemetry to enable reusable, consistent analytics and model development
  • Collaborate cross-functionally to support new studies and launches, ensuring data architecture meets evolving research and product needs

Requirements

  • 2+ years building data/ML infrastructure in production (data engineering, ML platform, or MLOps)
  • Strong Python engineering and SQL fluency
  • proven ability to write clean, maintainable, high-performance code
  • Experience with distributed processing (e.g., Spark, Flink, Ray) and workflow orchestration (e.g., Airflow or similar)
  • Experience with cloud infrastructure and data systems (e.g., AWS/GCP
  • object storage
  • streaming systems
  • warehouses/lakehouses)
  • Practical understanding of ML development workflows (training/eval/inference), and how platform decisions affect model velocity and reliability
  • Strong debugging skills in Linux environments and comfort operating systems that run 24/7

Nice to have

  • Experience with time-series, sensor, or biometrics data (wearables/IoT) and handling irregular sampling, missingness, and calibration
  • Experience building feature stores, online/offline parity systems, and low-latency inference pipelines
  • Experience with containerization and deployment (Docker/Kubernetes), and ML CI/CD practices
  • Exposure to regulated or research environments (clinical studies, validation processes, privacy/security constraints)

What we offer

  • Equity participation
  • Periodic equity refreshments based on performance
  • Your own Pod product
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