Remote RL Engineer - Reinforcement Learning Expert

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

Job summary

Bright Vision Technologies in the United States seeks a Machine Learning Engineer - RL to design, train, and deploy RL systems for high-impact decision-making problems. The role requires deep familiarity with reinforcement learning, simulation environments, reward modeling, and scalable training and evaluation of policies.

The ideal candidate has research depth and engineering pragmatism, with experience bringing RL solutions from lab to production where stability and safety are critical.

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.

Responsibilities

• Design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient.
• Work with simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale.
• Translate RL research into production with emphasis on stability, safety, and ongoing improvement.

Skills

Python

Deep learning

RL theory

Probability

Optimization

GPU training

Reward modeling

Simulation environments

Education

Master's or PhD in CS/ML or related field

Tools

PyTorch

TensorFlow

RL libraries

Job description

Bright Vision Technologies in the United States seeks a Machine Learning Engineer - RL to design, train, and deploy RL systems for high-impact decision-making problems. The role requires deep familiarity with reinforcement learning, simulation environments, reward modeling, and scalable training and evaluation of policies.

The ideal candidate has research depth and engineering pragmatism, with experience bringing RL solutions from lab to production where stability and safety are critical.