Applied Reinforcement Learning Engineer
USD 150K-160K Mid-level Full Time
Tasks
- Architect multi step reasoning agents with tool calling
- Build pipelines convert human labeled traces into RL training data
- Design RL environments simulate enterprise workflows
- Design reward functions and verifiers validation frameworks
- Train LLM agents with RLHF and policy optimization
- Translate RL research into production systems
Perks/Benefits
- Collaboration with industry leaders
- Hybrid remote work
- Open source contributions support
- Research Publications Opportunities
- Work on Enterprise AI Projects
Skills/Tech-stack
A2C | A3C | Actor-critic | Agent systems | BCQ | Behavioral cloning | CQL | Curiosity Driven Exploration | DPO | Deep Q-Network | Dense Reward | Domain Randomization | Double Deep Q Network | Dreamer | Dueling Deep Q Network | Eligibility Traces | Entropy Regularization | GAIL | Goal Conditioned Policy | Gymnasium | Hierarchical reinforcement learning | Human Feedback | IPO | IQL | Intrinsic Motivation | JAX | KTO | Markov Decision Process | Model Training | MuZero | Multi-Agent | Multi-Agent Systems | Offline Reinforcement Learning | OpenAI Gym | Options Framework | PPO | Policy Gradient | Policy Optimization | Potential Based Reward Shaping | Preference Learning | Proximal Policy Optimization | PyTorch | Python | Q-learning | REINFORCE | RLHF | Reinforcement Learning | Reward Model | Reward engineering | Reward model training | Reward shaping | Rllib | Simulation to Real | Simulation-to-Real transfer | Soft Actor Critic | Sparse Reward | Stable Baselines | TD Lambda | TD zero | TRPO | Temporal Difference Learning | TensorFlow | Trust Region | Trust Region Policy Optimization | UCB | World Models
Education
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