Reinforcement Learning Engineer
Location
California
Posted
11 days ago
Salary
0
Seniority
Senior
Job Description
Reinforcement Learning Engineer
Bright Vision Technologies
• 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. • Apply offline RL and imitation learning techniques where exploration is costly or unsafe. • Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant. • Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems. • Optimize training stability and sample efficiency through algorithmic and engineering improvements. • Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases. • Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight. • Collaborate with applied scientists and product teams to identify high-value RL use cases. • Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users. • Document methodology, design decisions, and operational characteristics for internal stakeholders. • Stay current with RL research and translate promising techniques into production-ready solutions.
Job 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.
- Strong written and verbal communication skills.
- Track record of shipping or publishing impactful RL work.
Benefits
- Comprehensive benefits
- Competitive compensation packages
- Supportive work-life balance
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