Remote Reinforcement Learning Systems Engineer

Bright Vision Technologies; Plymouth (MN); Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an AI Learning Systems Engineer to design, train, and deploy reinforcement learning–based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms and scalable training workflows.

The ideal candidate has research depth and pragmatic engineering skills, with experience taking RL solutions from lab to production where stability, safety, and

Qualifications

• Master’s or PhD in Computer Science, ML, or related field.
• 6+ years of RL research and engineering experience.
• Strong Python proficiency and modern DL 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.

Skills

Python

Reinforcement learning

RL libraries

GPU training

Simulation environments

Reward functions

RL theory

Communication

6+ years RL experience

Education

Master’s or PhD in Computer Science/ML or related field

6+ years of RL research and engineering experience

Tools

Major RL library / in-house RL stack

Job description

Bright Vision Technologies is seeking an AI Learning Systems Engineer to design, train, and deploy reinforcement learning–based systems for high-impact decision-making problems where supervised learning alone is insufficient. The role requires deep familiarity with modern reinforcement learning algorithms and scalable training workflows.

The ideal candidate has research depth and pragmatic engineering skills, with experience taking RL solutions from lab to production where stability, safety, and