ML Systems Engineer, Eight Sleep

• 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

Desirable

• 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
• 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