AI Engineer – Physical AI & Swarm UAVs

Are you passionate about bridging the gap between digital algorithms and physical reality? We are looking for an ambitious Junior AI Engineer to join our team building cutting-edge Physical AI systems.In this role, you will design, train, and deploy Reinforcement Learning (RL) models for autonomous multi-drone (swarm) systems. You will play a hands-on role—from training agents in high-fidelity simulated environments to running field trials on real hardware. If you want to push the boundaries of multi-agent autonomy and see your code fly in the physical world, this is the place for you.Key ResponsibilitiesRL Model Training & Optimization: Develop, train, and evaluate single-agent and multi-agent reinforcement learning algorithms for swarm UAV navigation, control, and mission strategy.Simulation to Real (Sim2Real): Utilize robotics simulators to simulate complex environments, refine reward functions, and execute Sim2Real transfer for physical UAV deployment.Field Testing & Data Collection: Participate in field test trials at outdoor testing sites (located on the outskirts of Bengaluru) to validate algorithm performance, troubleshoot edge cases, and collect flight data.Algorithm Pipeline Integration: Collaborate with software and control systems teams to translate trained PyTorch/TensorFlow models into deployable C++/Python packages for flight hardware.Must-Haves (Basic Qualifications)Education: Degree (B.E./B.Tech/M.E./M.Tech) in an Engineering discipline (Computer Science, Aerospace, Mechanical, ECE, Robotics, Mechatronics, etc.).Core Competencies: Strong, demonstrable capabilities in three foundational pillars:Mathematics: Linear Algebra, Vector Calculus, Probability & Statistics, and Optimization principles.Computer Science: Data structures, algorithms, modular code design, and object-oriented programming in Python.Artificial Intelligence: Fundamental understanding of Machine Learning and Reinforcement Learning concepts (e.g., Markov Decision Processes, Policy Gradients, Q-Learning).Demonstrated Interest in Physical AI: Strong passion for applying AI to real-world physical platforms (drones, robotics, or autonomous vehicles). Academic projects, capstone work, or personal side projects are a big plus.Travel & Testing Willingness: Willingness to travel regularly to field testing grounds located at the outskirts of Bengaluru for real-world UAV flight trials.Work Preference: Ability to thrive in a hybrid work environment (mix of remote/office software development and hands-on outdoor field testing).Good-to-Haves (Nice-to-Haves)Robotics Simulators: Hands-on experience with robotics or physics simulation environments (e.g., Gazebo, AirSim, NVIDIA Isaac Gym, PyBullet, Webots).Swarm & Multi-Agent AI: Exposure to Multi-Agent Reinforcement Learning (MARL) algorithms (e.g., MAPPO, MADDPG) or decentralized consensus algorithms.Robotics Frameworks: Familiarity with ROS / ROS2 architecture and node communication.UAV Hardware & Flight Controllers: Experience with open-source flight stacks like PX4 or ArduPilot, or basic hands-on experience assembling/debugging UAV hardware.Systems Programming: Proficiency in C++ in addition to Python for real-time edge execution.What We OfferWork at the Cutting Edge: Direct exposure to bleeding-edge Physical AI technologies and multi-drone autonomy.Real-World Impact: Watch your algorithms control hardware in real-world field environments rather than staying confined to benchmark datasets.Mentorship & Growth: Collaborate closely with senior AI researchers and robotics hardware engineers in an environment built for fast learning and innovation.