Remote Reinforcement Learning Systems Engineer

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

Benefits offered by this job

Equal Opportunity Employer

Job summary

Bright Vision Technologies is seeking an AI Learning Systems Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems. You will work on reinforcement learning algorithms, simulation environments, and reward modeling to scale production-grade policies.

The ideal candidate has 6+ years of RL research and engineering experience, strong Python and deep learning skills, and a track record of shipping RL work.

Qualifications

• Master’s or PhD in CS/ML or equivalent experience.
• Six+ years of RL research and engineering experience.
• Strong Python and modern deep learning frameworks.
• Hands-on experience with at least one major RL library.
• Foundations in probability, optimization, and RL theory.
• Experience designing reward functions in non-trivial environments.
• Familiarity with simulation environments and large-scale data 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 decisions.
• Take RL solutions from research to production with stability and safety.
• Collaborate with cross-functional teams to operationalize RL.
• Evaluate policies at scale and iterate improvements.

Skills

Reinforcement Learning

Python

Deep Learning

RL libraries

GPU clusters

Communication skills

RL research

Reward modeling

Simulation environments

Probability/Optimization

Education

Master’s or PhD in Computer Science, Machine Learning, or related field

Tools

RL Library

GPU frameworks

Job description

Bright Vision Technologies is seeking an AI Learning Systems Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems. You will work on reinforcement learning algorithms, simulation environments, and reward modeling to scale production-grade policies.

The ideal candidate has 6+ years of RL research and engineering experience, strong Python and deep learning skills, and a track record of shipping RL work.