Remote Reinforcement Learning Engineer: Scale & Deploy AI

Bright-Vision-Technologies; United States; Remote; USD 96,000 - 120,000; Full time

Job summary

Bright Vision Technologies is seeking a Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems. The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and engineering complexity of training and evaluating policies at scale.

The ideal candidate will have 8+ years of RL research and engineering experience, strong Python skills, and hands-on experience with major RL libraries, ready to

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 where supervised learning alone is insufficient.
• Take RL solutions from the lab to production, ensuring stability and safety, with ongoing improvement at scale.

Skills

Python

Deep learning frameworks

RL libraries

GPU clusters experience

Communication skills

Shipping RL work

Education

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

Equivalent applied experience

Tools

RL library / in-house RL stack

Job description

Bright Vision Technologies is seeking a Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems. The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and engineering complexity of training and evaluating policies at scale.

The ideal candidate will have 8+ years of RL research and engineering experience, strong Python skills, and hands-on experience with major RL libraries, ready to