AI Robotics AI Engineer Interview Questions

Discuss decentralized architecture, DDS middleware, improved security, and real-time capabilities.

Provide a simple robotics example for each: supervised for classification, unsupervised for clustering sensor data, RL for a robot learning to walk.

Address latency requirements, power/memory constraints, real-time operation, and sensor noise/variability.

Define transferring policies learned in simulation to the real world, and explain its importance for safe, scalable, and data-efficient training.

Cover image capture, preprocessing, model inference (e.g., YOLO), post-processing (NMS), and publishing detections as a ROS message.

Discuss domain randomization, analyzing failure modes (lighting, textures), fine-tuning with a small real-world dataset, and sim-to-real gap analysis.

Describe calibration, projection, and late/early fusion strategies, perhaps referencing architectures like PointPainting or Frustum PointNets.

Define each, note model-based's sample efficiency with a learned dynamics model, but its added complexity and potential for model exploitation.

Cover export to ONNX, using TensorRT's trtexec tool, calibration for INT8 precision, and integration into a ROS2 C++/Python node.

Discuss simulation testing, formal verification methods, runtime monitors, safe fallback behaviors, and adherence to safety standards like ISO 13849.

Compare end-to-end learning (data hungry, harder to debug) vs. modular design (easier to inspect/improve parts, potential information bottlenecks).

Explain data collection via teleoperation, state-action mapping, handling of variations, and deployment with appropriate generalization checks.

Talk about Git for code, DVC for data/models, Docker for environment consistency, and simulation-based regression tests in CI pipelines (e.g., GitHub Actions).

Discuss model pruning, knowledge distillation, hardware-specific optimization (TensorRT, Core ML), reducing input resolution, and benchmarking.

Describe a pipeline: language grounding via VLM for object/relation identification, task planning with an LLM or hierarchical planner, and integration with a motion planner. Discuss handling of ambiguity.

Touch on online learning, experience replay, elastic weight consolidation, and modular architectures that isolate knowledge.

Propose a composite reward (forward progress, energy efficiency, stability penalties) and discuss techniques like reward shaping, adversarial scenarios, and human feedback.

Discuss over-the-air updates, model performance monitoring (MLOps), fleet-level simulation testing, A/B testing new models, and centralized data collection for retraining.

Contrast disembodied AI with AI that perceives and acts in a physical world. Highlight challenges like the cost of data, real-time constraints, partial observability, and generalization to physical laws.

Discuss domain randomization, high-fidelity physics engines (e.g., NVIDIA PhysX), procedural generation of assets, and the role of generative models in creating synthetic data.

Address bias in perception models, edge-case decision-making, accountability, privacy concerns, and the need for human-in-the-loop oversight.

The candidate should demonstrate research awareness, critical thinking about the method's strengths/weaknesses, and a practical plan for implementation and validation in simulation.

Outline a systematic approach: logging and visualizing intermediate AI outputs (e.g., bounding boxes, segmentation masks), checking for data drift, running unit tests on the model in isolation, and using simulation to reproduce the issue.

Immediate: switch to a safe, fallback behavior. Long-term: analyze the failure, add lighting variations to training data, implement a more robust model or sensor fusion, and develop better out-of-distribution detection.

Discuss evaluating its capabilities, distilling its knowledge into a smaller model, using it as a 'teacher' in a simulation, or architecting a system where it handles planning offline/perception in a non-critical loop.

Compare data availability (demonstrations vs. reward function), safety during training, sample efficiency, task complexity, and the need for generalization.

Discuss uncertainty-based sampling, detecting novel scenarios or high-loss instances, data compression techniques, and the trade-off between storage and communication costs.

Talk about auditing the dataset, implementing fairness metrics, sourcing diverse and representative data, retraining with balanced sampling, and establishing ongoing monitoring protocols.

Explain researching the sensor's data format, adapting or developing new models (e.g., for asynchronous event streams), creating simulation models for it, and evaluating its added value against cost/complexity.

Discuss intent signaling (e.g., via lights or sounds), compliant motion control, the 'social robot' navigation layer, and keeping a human supervisor in the loop for critical decisions.

Focus on robustness, monitoring, logging, automated testing, model versioning, update mechanisms, and performance profiling under load, rather than just algorithmic accuracy.

Advocate for a balanced view: analyze the benchmark's realism, consider your system's interpretability, safety, and generalization to your specific use case. Propose a targeted experiment or hybrid approach rather than a full rewrite.

Discuss meta-learning (MAML), leveraging large pre-trained models for feature extraction, and using simulation to augment the few demonstrations with synthetic variations.

Should cover: understand paper, reproduce in PyTorch, evaluate on standard and custom datasets, export to ONNX, optimize with TensorRT, build ROS2 package, write node with image subscriber and bounding box publisher, test in simulation, deploy to real robot with logging.

Describe creating a controlled testbed (e.g., a office loop), recording a sensor dataset (bag file), running each algorithm offline on the same data, and defining metrics for comparison (accuracy, CPU load, robustness).

Explain curating a domain-specific image-text dataset (possibly with synthetic captions), using contrastive learning or instruction tuning techniques, and evaluating on a retrieval or grounding task.

Mention CAD import, defining dynamics (joints, friction, meshes) in a simulator like Isaac Sim, running domain randomization, calibrating the sim-to-real gap with physical experiments, and generating parallel data for training.

Discuss tools like DVC, data lakes (S3), annotation tools (Labelbox, CVAT), scripts for cleaning/augmentation, and ensuring traceability from a model version to the data it was trained on.

Describe using the LLM for high-level reasoning/planning in a separate process, caching common queries, streaming responses, and having a fast, deterministic fallback planner for time-critical actions.

Talk about testing perception nodes with synthetic/mock data, testing planning modules with fixed world states, and using a simulation environment for full integration tests as part of CI/CD.

Discuss logging predictions and confidence scores, tracking key performance metrics over time, setting up alerts for performance drops, and using techniques like outlier detection on input data streams.

Outline starting with SOTA models, profiling their latency/accuracy on target hardware, considering model complexity and data availability, and potentially using neural architecture search (NAS) tools.

Explain creating adversarial scenarios (e.g., sensor noise, edge-case objects, sudden obstacles), running thousands of automated episodes, analyzing failure logs, and iteratively improving the system's robustness.

Assesses communication skills, ability to simplify without losing essence, and empathy for different roles in a cross-functional team.

Looks for resilience, technical adaptability, learning from failure, and the ability to systematically debug and try new hypotheses.

Evaluates passion, learning habits (e.g., arXiv, conferences, open-source projects), and the ability to translate research into practical value.

Tests conflict resolution, systems thinking, and the ability to bridge technical cultures by focusing on shared goals, data, and trade-off analysis.

Probes ethical awareness, foresight, and the candidate's framework for reasoning about bias, safety, and societal impact in their engineering work.