Sr. ML Ops Engineer

This role focuses on building data infrastructure, training pipelines, and internal tooling to accelerate ML iteration for autonomous drone inventory tracking. You will own ML data infrastructure from robot to training run, build model evaluation and regression testing, and automate model retuning loops.

Responsibilities

• Build and maintain the data pipeline infrastructure that consolidates internal infra, labeling tools, S3, and other data sources into a unified, queryable system
• Build tooling for dataset selection and curation that can programmatically target specific data (by environment, object type, etc.)
• Own ML data infra from robot to training run, accessible to the ML team without backend engineering help
• Build model evaluation and regression testing infrastructure -- real metrics, not vibes or "someone complained in prod"
• Automate the model retuning loop for standard tasks so ML engineers can be mostly hands-off on routine updates

Requirements

• 2-3 years shipping real production ML infrastructure for big datasets, not just scripts
• Experience building distributed data pipelines that consolidate multiple sources
• Demonstrated understanding of data flow from raw collection, labeled training set, to trained models
• Experience building systems from scratch, or contributed heavily to a small-team infra build where the playbook didn't exist
• Ability to thrive in a startup environment with high ambiguity. You'll figure out what to build

Nice to have

• Experience setting up annotation tooling and workflows
• Background in robotics autonomy and computer vision
• Experience integrating with tools like Kubeflow, SLURM, or similar for scalable training workflows