Reinforcement Learning Engineer
New
100% Remote (Continental United States)Full-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 6+ years
- Required Skills
- PythonDeep LearningDistributed Systems
Requirements
- Master’s or PhD in Computer Science, Machine Learning, or a related field; or equivalent applied experience.
- Six or more years of combined RL research and engineering experience.
- Strong proficiency in Python and modern deep learning frameworks.
- Hands-on experience with at least one major RL library or in-house RL stack.
- Solid understanding of probability, optimization, and the theoretical foundations of RL.
- Experience designing and tuning reward functions in non-trivial environments.
- Familiarity with simulation environments and large-scale experience collection.
- Experience training neural network policies on GPU clusters.
- Track record of shipping or publishing impactful RL work.
Responsibilities
- Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
- Develop, calibrate, and maintain simulation environments suitable for large-scale agent training.
- Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
- Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
- Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant.
- Build scalable training infrastructure for distributed RL.
- Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
- Collaborate with applied scientists and product teams to identify high-value RL use cases.
- Monitor deployed policies and models in production for drift and regression.
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