About this role
What You Will Get To Do
- Own the end-to-end inspection behavior success metric.
- Build the evaluation loop: golden-labeled datasets, seeded-fault runs, and regression suites at feature, system, and release level that gate every release candidate.
- Turn field evidence into training and evaluation sets.
- Map technical performance to the metrics the customer measures.
- Make failures legible: triage mission logs, attribute each miss to navigation, localization, perception, or behavior, and route it to the owning team with evidence.
What You Have
- MS in computer science, robotics, aerospace engineering, or a related field, with 3+ years in robotics or ML engineering.
- Strong Python, working C++, and comfort with ROS 2 logs, bags, and replay tooling.
- Experience evaluating and fine-tuning vision or vision-language models (PyTorch, LoRA or similar), including building the datasets and metrics behind them.
- Statistical rigor: knowing the difference between a benchmark number and a field result, and how to design an experiment that tells them apart.
- Data pipeline skills to move evidence from robot to training set to dashboard.
What Will Set You Apart
- Authored or maintained a robotics benchmark, evaluation suite, or challenge.
- Simulation-based evaluation and sim-to-real validation.
- MLOps for on-edge models: quantization, versioning, and rollout to a fleet.
- Industrial inspection, gauge reading, or anomaly detection experience.