Machine Learning Engineer – RL

Bright Vision Technologies • Wildwood, MO

9d • $155,000 - $180,000 • Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Engineer focused on reinforcement learning (RL) to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role calls for familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale. The ideal candidate combines research depth with engineering pragmatism and has taken RL solutions from research into production, where stability, safety, and ongoing improvement are critical.

This is a full-time, direct W2 position that is 100% remote within the United States. The salary range is $155,000–$180,000 annually. Candidates with six or more years of combined RL research and engineering experience are sought. The employer cannot 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 reinforcement-learning 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.
• Train and evaluate policies at scale, with attention to stability, safety, and ongoing improvement.
• Design and tune reward functions in non-trivial environments.
• Use simulation environments and large-scale experience collection.
• Train neural network policies on GPU clusters.
• Ship or publish impactful reinforcement-learning work.

What This Job Offers

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