Reinforcement Learning Engineer

Bright Vision Technologies•Fremont, CA

1d•Remote

About The Position

Bright Vision Technologies 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. As we continue to grow, we’re looking for a skilled Reinforcement Learning Engineer to join our dynamic team and contribute to our mission of transforming business processes through technology. This is a fantastic opportunity to join an established and well-respected organization offering tremendous career growth potential.

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.

Nice To Haves

Experience with RLHF for large language models.
Familiarity with multi-agent RL or hierarchical RL.
Exposure to robotics, control systems, or autonomous driving.
Publications in RL or related research venues.
Open-source contributions to RL libraries or environments.

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.
Document methodology, design decisions, and operational characteristics for internal stakeholders.
Stay current with RL research and translate promising techniques into production-ready solutions.