Remote Reinforcement Learning Engineer – AI Systems

Visa Hunt; United States; On-site; USD 100,000 - 150,000; Full time

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. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and engineering complexity of training policies at scale.

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

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.
• Translate research into production-ready RL solutions emphasizing stability and safety.
• Evaluate and improve policies at scale, with robust monitoring and iteration.

Skills

Python

Deep learning frameworks

Reinforcement learning

RL libraries

GPU clusters

Communication skills

Research & engineering

Education

Master's or PhD in CS/ML

Equivalent applied experience

Tools

RL library

In-house RL stack

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. The role requires deep familiarity with modern reinforcement learning algorithms, simulation environments, reward modeling, and engineering complexity of training policies at scale.

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