MLOps Engineer building production ML infrastructure, pipelines, and monitoring for exacare ai’s post - acute care AI platform. Scaling reliable systems that help healthcare teams make safer placement decisions.
About the role
- ML Ops Engineer building data pipelines, datasets, and training workflows for AeroVect’s autonomous ground-handling systems. Improving perception tooling, model diagnostics, and cloud efficiency.
Responsibilities
- Build and maintain data pipelines to ingest field data (rosbags, sensor logs, telemetry) from the fleet
- Convert raw field data into curated, versioned datasets for the perception team
- Own dataset management, including storage, indexing, querying, and vending datasets
- Set up training workflows and optimize cloud costs
- Build tooling to accelerate perception engineers' workflows, including fast data access, reproducible experiments, and automated evaluation pipelines
- Generate metrics and diagnostics to track dataset health, model performance, and pipeline reliability
Requirements
- Bachelor's or Master's degree in Computer Science, Robotics, Data Engineering, or a related field
- Strong Python proficiency and working knowledge of ROS2
- Working knowledge of docker and other DevOps tools
- Familiarity with cloud storage and compute (AWS - S3, EC2, etc.)
- Understanding of ML workflows and dataset versioning
- 2+ years of MLOps or data infrastructure experience, ideally in robotics or autonomous systems (preferred)
- Experience with Weights & Biases, rosbag data, and large-scale sensor datasets (preferred)
- Working knowledge of C/C++ (preferred)
- Experience supporting perception or ML research teams (preferred)
Benefits
- Equity
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