Reinforcement Learning Engineer

Reinforcement Learning Engineer

Full-Time | Core Team

LAT Aerospace | Perception & Autonomy

At LAT Aerospace, we’re building India’s first clean-sheet hybrid-electric STOL aircraft and a next-generation autonomy stack powering aircraft for complex missions. Our team is kicking off a new chapter — developing the full flight-control, obstacle-avoidance, GNSS-denied navigation, and swarm-coordination layers that will define LAT’s autonomy architecture.

As an RL Engineer, you will design, train, and deploy policy learning systems that optimize guidance decisions for multi-agent swarms. Your algorithms will directly shape LAT’s next-generation autonomous behaviors.

What you’ll do?

• Develop reinforcement learning and policy-learning algorithms that improve swarm guidance, collision avoidance, task allocation, and distributed decision-making.
• Integrate RL with classical control and planning — combining learned value functions, cost shaping, and residual control to boost agility, robustness, and safety.
• Build scalable training pipelines for multi-agent RL using domain randomization, curriculum learning, simulation rollouts, and extensive scenario generation.
• Own sim-to-real transfer, ensuring policies trained in simulation generalize to real-world UAV dynamics, uncertainties, and edge-case environments.
• Design multi-agent coordination behaviors such as formation flight, coverage, pursuit/avoidance, collaborative mapping, and decentralized cooperation under minimal communication.
• Run frequent field tests to evaluate learned policies on actual UAVs, gather flight data, and iterate rapidly.
• Develop evaluation frameworks and debugging tools to diagnose RL failures, mode collapse, instability, or unsafe behaviors.

What we're looking for?

• Strong fundamentals in reinforcement learning, policy optimization (PPO, SAC, TD3), or multi-agent RL.
• Experience with robotics autonomy, motion planning, or control systems.
• Proficiency in Python with RL libraries (PyTorch, JAX, RLlib, Stable Baselines, CleanRL, etc.).
• Hands-on experience with robotics simulation environments — Isaac Lab, Gazebo, MuJoCo, PyBullet, or custom simulators.
• Comfort with integrating RL modules into larger autonomy frameworks and evaluating them on real systems.

Why LAT?

• Core-team role building the autonomy brain of India’s most ambitious aerospace startup.
• Ownership, speed, and the opportunity to define a new class of systems from scratch.