RL Engineer

Bright Vision Technologies • San Diego, CA

22h • $80,000 - $100,000 • Remote

About The Position

Bright Vision Technologies is seeking an RL Engineer to design, train, and deploy reinforcement-learning-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role involves applying modern reinforcement learning algorithms, simulation environments, reward modeling, and scalable training and evaluation methods. The successful candidate will combine research depth with engineering pragmatism, taking RL solutions from research into production with attention to stability, safety, and ongoing improvement.

The position is full-time, direct W2, and 100% remote within the United States. The annual salary range is $80,000–$100,000. Candidates who are U.S. citizens, Green Card holders, EAD holders, or eligible for an H-1B transfer are encouraged to apply; the company is unable to sponsor new H-1B visa petitions for this position.

Requirements

• Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience.
• Six or more years of combined reinforcement learning research and engineering experience.
• Strong proficiency in Python and modern deep learning frameworks.
• Hands-on experience with at least one major RL library or an in-house RL stack.
• Solid understanding of probability, optimization, and the theoretical foundations of reinforcement learning.
• 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, train, and deploy reinforcement-learning-based systems for high-impact decision-making problems.
• Apply modern reinforcement learning algorithms, simulation environments, and reward modeling.
• Engineer training and evaluation of policies at scale.
• Take RL solutions from research into production, supporting stability, safety, and ongoing improvement.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior