Technical practice
Multi-Object Tracking (MOT) in Crowded Environments
What to coverExplain your approach to maintaining ID stability during occlusions and detector gaps. Discuss the trade-offs between classical Bayesian filtering (e.g., EKF/UKF) and modern query-based or transformer-based tracking methods.
Generic Open-Set World Understanding
What to coverHow do you implement class-agnostic obstacle detection to ensure safety against unknown objects? Discuss the use of occupancy grids or occupancy networks and how they integrate with learned detection branches.
Motion Prediction and Uncertainty Estimation
What to coverDescribe how you model multi-modal trajectory forecasting for dynamic agents like pedestrians or cyclists. How do you quantify and communicate prediction uncertainty to the downstream planning stack?
Sensor Fusion and Temporal Consistency
What to coverDetail your strategy for integrating asynchronous data from LiDAR, radar, cameras, and IMU. Address how you handle time-synchronization and calibration issues to maintain a consistent world representation.
Deploying Foundation Models to Edge Hardware
What to coverWhen adapting VLM/VLA models for on-robot deployment (e.g., NVIDIA Orin), what techniques do you use for distillation, quantization, or fine-tuning? How do you validate these models against hallucinations and latency constraints?
Performance Metrics and Quality Gates
What to coverDefine the key metrics you use to evaluate perception and prediction systems (e.g., MOTA, ADE/FDE, occupancy recall). How do you set quality gates to prevent regressions during fleet-log replay and simulation?
Hybrid Perception Architectures
What to coverThe role emphasizes balancing modular classical methods with end-to-end learning. Suggest how to design a system where classical geometric methods serve as safety fallbacks for deep learning models.
Real-time Embedded Optimization
What to coverDiscuss your experience optimizing perception pipelines using tools like TensorRT. How do you balance the computational cost of complex models against the strict latency requirements of a delivery robot?
Behavioural practice
Handling Research-to-Production Transitions
What to coverDescribe a situation where you had to take a promising research prototype and make it production-ready. Focus on how you handled performance bottlenecks, reliability requirements, and cross-functional feedback.
Root-Cause Analysis in Production
What to coverProvide an example of a time you identified a failure in a deployed robotics system using fleet logs. Explain your process for isolating the issue, validating the fix in simulation, and ensuring it did not recur.
Cross-Functional Collaboration
What to coverDescribe a time you worked with planning or integration teams to resolve a disagreement regarding perception output interfaces. How did you ensure the final design met the needs of both teams while maintaining system safety?
Navigating Technical Trade-offs
What to coverGive an example of a project where you had to choose between a high-performance learning-based approach and a more interpretable classical method. What factors influenced your decision and what was the outcome?