Machine Learning Engineer – RL
Tasks
- Apply offline reinforcement learning and imitation learning
- Build scalable training infrastructure for distributed reinforcement learning
- Collaborate with scientists and product teams to identify reinforcement learning use cases
- Design and implement reinforcement learning solutions for sequential decision making
- Design evaluation protocols including out of distribution and adversarial test cases
- Develop calibrate and maintain simulation environments for large scale agent training
- Document methodology design decisions and operational characteristics
- Engineer reward functions and reward shaping strategies
- Implement and evaluate policy gradient actor critic off policy and offline reinforcement learning
- Implement safety mechanisms including constraint enforcement and human in the loop oversight
- Monitor deployed policies for drift regression and unintended behaviors
- Optimize training stability and sample efficiency
- Stay current with reinforcement learning research and translate into production
- Use reinforcement learning from human feedback and direct preference optimization for large language model fine tuning
Perks/Benefits
Skills/Tech-stack
Actor-critic | Adversarial Testing | Constraint Optimization | Deep learning | Distributed Training | Distribution testing | Experience Replay | GPU Computing | Human-in-the-loop | Imitation Learning | Off Policy | Off Policy Learning | Offline Reinforcement Learning | Optimization | Out of Distribution | Out of Distribution Testing | Policy Gradient | Policy learning | Probability theory | Python | Reinforcement Learning | Reward engineering | Reward shaping | Simulation | The Loop
Education
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