Senior Reinforcement Learning Engineer - Remote

Bright Vision Technologies; Frisco (TX); Remote; USD 155,000 - 180,000; Full time

Job summary

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company is seeking 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 ideal candidate has deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the

Qualifications

• 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.

Responsibilities

• Design, train, and deploy RL-based systems for high-impact decision-making problems.
• Work with simulation environments, reward modeling, and large-scale training.
• Bridge research depth with engineering pragmatism to productionize RL solutions.

Skills

Python

Deep learning

Reinforcement learning

GPU training

Communication

Education

Master's or PhD in CS/ML

Tools

RL libraries

Job description

Bright Vision Technologies is a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States. The company is seeking 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 ideal candidate has deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and the