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 Responsibilities
RL 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 Offer
Work 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.

Work arrangement
No