Machine Learning Engineer – RL

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The Machine Learning Engineer - RL will design, train, and deploy reinforcement learning systems for complex decision-making problems, applying expertise in algorithms, simulation environments, reward modeling, and large-scale policy training. The role also involves taking reinforcement learning solutions from research into production with an emphasis on stability, safety, and continuous improvement.

Responsibilities:

• We are looking for a Machine Learning Engineer - RL to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient
• The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale
• The ideal candidate has both research depth and engineering pragmatism, with experience taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical

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
• 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