MS in computer science, robotics, aerospace engineering, or related field.
3+ years of experience in robotics or ML engineering.
Proficient in Python and working knowledge of C++.
Familiarity with ROS 2 logs, bags, and replay tooling.
Experience with vision or vision-language models, including dataset creation.
Responsibilities
Own the end-to-end inspection behavior success metric.
Build evaluation loops with golden-labeled datasets and regression suites.
Transform field evidence into training and evaluation sets.
Map technical performance to customer metrics.
Triage mission logs and attribute failures to specific teams.
Benefits
Generous salary range based on individual background and experience.
Opportunities for professional growth in a cutting-edge field.
Full Job Description
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.
Our salary range is generous and we consider each individual's background and experience when determining final compensation. Base pay may vary based on role scope, job-related knowledge, skills, experience, and the Irvine, California market.
