Reinforcement Learning Engineer

Job summary

Bri*** is a forward-thinking software development company dedicated to building innovative solutions that help businesses automate and optimize their operations. We leverage cutting-edge technologies to create scalable, secure, and user-friendly applications.

Benefits

Qualifications

• The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical.
• 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
• Experience with RLHF for large language models
• Familiarity with multi-agent RL or hierarchical RL

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
• 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

Skills

Degrees

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