About this role
Responsibilities:
- Define Validation Metrics: Establish rigorous, statistically sound KPIs and performance benchmarks for camera, LiDAR, Radar, and sensor-fusion perception stacks (e.g., precision/recall, mAP, latency, tracking MOTA).
- Build Automated Testing Pipelines: Design and maintain scalable, automated regression testing pipelines that evaluate perception algorithms on massive real-world and synthetic datasets.
- Ground Truth Generation: Oversee and optimize the creation of high-fidelity ground truth data, using automated labeling tooling, offline perception models, and manual curation.
- Edge-Case Mining: Identify, categorize, and build a library of challenging real-world scenarios, sensor degradations (e.g., lens flare, heavy rain, occlusion), and long-tail anomalies to stress-test the perception system.
- Root-Cause Analysis: Partner closely with the Perception ML team to debug validation failures, trace anomalies back to data or algorithmic root causes, and propose data-driven solutions. Close the loop with model training.
- Simulation & Tooling Development: Collaborate with the Simulation team to develop realistic sensor models and synthetic scenarios that bridge the gap between simulation and real-world validation.
- Safety Documentation: Generate comprehensive verification and validation reports to support safety cases, regulatory compliance, and software release readiness.
Required Skills:
- Education: Bachelor’s or Master’s degree in Computer Science, Robotics, Electrical Engineering, Aerospace Engineering, or a related quantitative field.
- Experience: 2+ years of professional experience in testing, validation, or development of robotics/autonomous systems, or computer vision models.
- Programming: Strong proficiency in Python (especially NumPy, Pandas, Pytest) and/or C++ .
- Data Analysis: Experience manipulating and visualizing large scale datasets (SQL, data lake queries) and utilizing statistical methods to evaluate model performance.
- Domain Knowledge: Solid understanding of core computer vision concepts, deep learning principles, and spatial geometry (3D transformations, coordinate frames).
Preferred Skills:
- Experience with ROS / ROS2
- Familiarity with cloud computing infrastructure ( AWS, GCP, or Azure ) and containerization ( Docker , Kubernetes).
- Experience with machine learning frameworks (PyTorch) and validation tools.
- Understanding of functional safety standards in automotive engineering (e.g., ISO 26262, ISO 21448 / SOTIF).
- Experience working with CI/CD pipelines (Jenkins, Github CI) for automated testing.