Applied RL Engineer
Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. This is a full-time, direct W2 role offering 100% remote work within the U.S. U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply; new H-1B sponsorship is not available.
About the role
We are looking for a Reinforcement Learning Engineer 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.
Responsibilities
- Design, train, and deploy RL-based systems for decision-making problems
- Develop and tune reward functions in non-trivial environments
- Work with simulation environments and large-scale experience collection
- Train neural network policies on GPU clusters
- Take RL solutions from research to production with a focus on stability, safety, and continuous improvement
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
- Strong written and verbal communication skills
- Track record of shipping or publishing impactful RL work
Preferred Qualifications
- 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
Pay
$100,000–$150,000 annually