Machine Learning Engineer (Reinforcement Learning Systems)

Overview

About Deccan AI

• Design, implement, and train RL agents using policy-gradient and value-based methods (PPO, DQN, SAC, etc.)
• Build and tune reward models and simulation environments for agent training
• Work on multi-agent RL systems and RLHF pipelines for model alignment
• Run large-scale training experiments and analyze agent behavior/failure modes
• Collaborate with research teams to translate RL research into production-ready systems

• 2–4 years of hands-on experience in reinforcement learning or related ML research
• Strong Python skills and experience with PyTorch, TensorFlow, or JAX
• Familiarity with Ray RLlib, Stable Baselines3, CleanRL, Gymnasium, or MuJoCo
• Solid understanding of core RL algorithms: PPO, DQN, SAC, actor-critic methods, reward shaping
• Bonus: published RL research, RLHF pipeline experience, or multi-agent systems work

• Direct exposure to frontier-lab-scale RL and alignment problems
• Work alongside top ML research and engineering talent
• High-ownership role with real technical depth
•