[ML Ops Engineer Application ]
WATER is a Biophysical Intelligence research company. Our mission is to teach matter to respond to life: we study how the human body and the physical world respond to each other, and we build machines that sense a body, model its state, and respond in real time. CAMA, an adaptive bed, and FLOW, an adaptive chair, are the first surfaces this research runs on. The work spans three research directions: machine learning and mathematics, bio research, and materials and electronics. The long-range outcomes we care about are human longevity and human experience.
You own the infrastructure behind the models. You'll work on data pipelines from device fleets, training infrastructure, experiment tracking, model registry and lineage, and the OTA path for model updates.
In This Role You Will
- -Own the data pipeline: ingestion from device fleets, storage, versioning, labelling infrastructure, and tooling that makes a dataset queryable.
- -Own training infrastructure — experiment tracking, reproducible runs, distributed training, GPU scheduling.
- -Build model registry and lineage. Any deployed model should be traceable to the exact data and code that produced it.
- -Own the OTA path for model updates, including staged rollout and rollback, alongside firmware.
- -Instrument everything. Make cost, throughput and failure visible without anyone asking.
- -Keep the loop fast. A researcher's idea should reach a trained result the same day.
What We Hope You'll Bring
- -You have built infrastructure that other engineers depended on daily.
- -2+ years in ML infrastructure, data engineering or platform engineering.
- -Strong Python; comfortable with containers, orchestration and cloud infrastructure as code.
- -Experience with high-volume sensor or time-series data pipelines.
- -You automate rather than document a manual process.
- -Nice to have: distributed training at scale, feature stores, fleet telemetry, embedded OTA systems.