JOB DESCRIPTION Physical AI Engineer Edge AI, Autonomy and Simulation Location: New Delhi Employment Type: Full Time Experience Level: 3+ Years Department: Engineering / AI and Autonomy Role Purpose The engineer will turn research and system requirements into perception and autonomy software that performs reliably on real hardware. The position requires practical ownership from data collection and model development through onboard optimization, integration with drone software, simulation, and controlled hardware testing. Key Responsibilities ● Vision models: Develop, train, fine tune, and evaluate YOLO based object detection and tracking pipelines for drone imagery and video. ● Data and evaluation: Build labeling, augmentation, dataset versioning, error analysis, and repeatable evaluation workflows across lighting, motion, altitude, and environmental conditions. ● Model optimization: Apply quantization, pruning, model conversion, and runtime profiling to meet latency, memory, compute, power, and thermal limits on target hardware. ● Reinforcement learning: Design training environments, reward functions, observations, actions, constraints, and evaluation procedures for autonomous behavior. ● Maneuver algorithms: Develop and test planning, guidance, obstacle avoidance, trajectory generation, and control support algorithms for complex maneuvers within defined safety limits. ● Embedded deployment: Package inference services and supporting software for embedded computers, including Yocto recipes, dependencies, startup, logging, configuration, and updates. ● System integration: Connect perception and autonomy modules with sensors, camera streams, telemetry, navigation estimates, autopilot interfaces, and ground tools. ● Simulation and testing: Set up realistic scenarios, run software in the loop and hardware in the loop tests, inject faults and edge cases, and compare simulated performance with recorded or physical tests. ● Software delivery: Write maintainable Python and C++ code, review changes, automate tests and builds, document design decisions, and produce reproducible releases. ● Cross functional support: Work with firmware, hardware, controls, and simulation engineers to define interfaces, review risks, reproduce issues, and support controlled field trials. Core Technical Areas Area Expected Knowledge and Application Computer vision YOLO training and inference, detection metrics, tracking, camera geometry, preprocessing, image quality, and failure analysis. Machine learning PyTorch or a comparable framework, dataset management, experiment tracking, generalization checks, and reproducible training. Edge optimization INT8 or mixed precision quantization, calibration, operator support, model export, profiling, and accuracy versus latency tradeoffs. Reinforcement learning Policies, environments, reward design, exploration, constraints, offline evaluation, and transfer from simulation to hardware. Autonomy algorithms State estimation inputs, path and trajectory planning, collision avoidance, maneuver constraints, guidance logic, and autopilot integration. Embedded Linux Yocto recipes and images, cross compilation, services, containers or packages where appropriate, device access, logs, and deployment. Simulation and test Scenario design, sensor and dynamics models, software in the loop, hardware in the loop, regression tests, telemetry replay, and performance measurement. Software engineering Python and C++, Git, code review, unit and integration tests, CI, clear interfaces, observability, and release documentation. Requirements Required Qualifications ● Bachelor's degree or equivalent practical experience in Computer Science, Electronics, Robotics, Aerospace Engineering, or a closely related discipline. ● At least 3 years of hands on experience developing and evaluating computer vision or autonomy software, including a YOLO family detector deployed to embedded hardware. ● Practical knowledge of quantization and runtime profiling, with evidence of measuring model accuracy, latency, memory use, and hardware resource limits. ● Experience developing algorithms for robotic or drone autonomy and testing them in simulation. ● Working knowledge of reinforcement learning concepts and the ability to define meaningful training and evaluation setups. ● Strong Python skills, working C++ skills, and experience integrating software on embedded Linux, including Yocto based builds. ● Ability to debug across models, software, sensors, system timing, and onboard hardware using logs and repeatable tests. Preferred Experience ● Experience with ArduPilot or PX4, MAVLink, ROS 2, camera and IMU data, GNSS, and telemetry pipelines. ● Deployment with ONNX, TensorRT, OpenVINO, TFLite, or hardware specific inference runtimes and accelerators. ● Synthetic data generation, domain randomization, visual simulation, and methods for reducing the gap between simulation and field performance. ● Development of software in the loop or hardware in the loop test benches, automated scenario suites, and telemetry replay tools. ● Experience with real time constraints, fault handling, safe behavior when perception is uncertain, and controlled field validation. ● AI or autonomy development for defence, aerospace, robotics, industrial automation, or other reliability sensitive systems.