Applied RL Engineer

Bright Vision Technologies • Orlando, FL

22h • $100,000 - $150,000 • Remote

About The Position

Bright Vision Technologies is hiring an Applied 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 modern reinforcement learning algorithms, simulation environments, reward modeling, and engineering systems for training and evaluating policies at scale. The ideal candidate combines research depth with engineering pragmatism and can take RL solutions from research into production, where stability, safety, and continuous improvement are important.

The position is fully remote within the United States and is full-time, direct W2, with an annual salary range of $100,000–$150,000. Applicants must be eligible to work in the United States; the employer encourages applications from U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates, but cannot sponsor new H-1B visa petitions.

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.
• Design and tune reward functions for non-trivial environments.
• Use simulation environments and large-scale experience collection to train and evaluate policies.
• Train neural-network policies on GPU clusters.
• Take RL solutions from research into production, supporting stability, safety, and ongoing improvement.

What This Job Offers

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