Machine Learning Engineer – RL

Bright Vision Technologies • Overland Park, KS

2d • $155,000 - $180,000 • Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Engineer – RL 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 RL algorithms, simulation environments, reward modeling, and training and evaluating policies at scale. The ideal candidate combines research depth with practical engineering experience, including bringing RL solutions into production with a focus on stability, safety, and ongoing improvement.

This is a full-time, direct W2 position that is 100% remote in the United States. The stated annual salary range is $155,000–$180,000. The employer is unable to sponsor new H-1B visa petitions; U.S. citizens, green card holders, EAD holders, and H-1B transfer candidates are encouraged to apply.

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 in non-trivial environments.
• Train and evaluate policies at scale, including training neural network policies on GPU clusters.
• Bring RL solutions from research into production, supporting stability, safety, and ongoing improvement.

What This Job Offers

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