Machine Learning Engineer (Reinforcement Learning)

The Role: You will design, train, optimize, and productionize Reinforcement Learning models at scale. This role is ideal for someone who is comfortable operating across research, engineering, and cloud systems while owning end‑to‑end RL development. You will work with complex datasets, ambiguous requirements, and modern distributed systems. Key Responsibilities: • Build RL solutions from data collection to deployment. • Run ML and RL experiments using Python ML libraries. • Implement and tune RL algorithms such as PPO, SAC, or DQN. • Design RL architectures using cloud tooling. • Optimize RL pipelines for speed, scale, and reliability. Requirements (Must Have): • Multiple years experience delivering production or real‑world RL solutions. • Strong knowledge of RL algorithm families and training procedures. • Hands‑on experience with Python, PyTorch or TensorFlow. • Experience working with real RL datasets and associated data schemas. • Experience shipping production‑ready ML code. Nice to Have: • Distributed RL (Ray RLLib). • Cloud platforms (GCP preferred). • CV‑based RL experience. • Backend engineering or MLOps exposure.