About the RoleWith a growing fleet of autonomous drones and an expanding customer base, we're now ready to multiply ML iteration speed and unblock more advanced ML product delivery.
We're hiring a
systems-oriented Senior Software Engineer to build the data infrastructure, training pipelines, and internal tooling that our ML team needs to move faster.
Specifically in this role you will:
- 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
This is a hybrid or remote role with periodic trips to HQ in Mountain View, CA.
Must Haves- 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 Haves- 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
