Remote Reinforcement Learning Engineer for Scalable Production AI

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

Job summary

Bright Vision Technologies, a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States, is seeking a Reinforcement Learning Engineer for a 100% remote role.

You will design, train, and deploy RL-based systems for high-impact decision-making problems, requiring 8+ years of experience and expertise in RL algorithms, simulation environments, reward modeling, and scaling policies.

Qualifications

• Master’s or PhD in CS/ML or related field, or equivalent applied experience.
• Six or more years of 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.
• Take RL solutions from research to production, ensuring stability, safety, and ongoing improvement.

Skills

Python

Deep learning

Reinforcement Learning

RL libraries

GPU training

Communication skills

Education

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

Tools

Simulation environments

GPU hardware

Job description

Bright Vision Technologies, a technology consulting and software development company delivering cloud, AI, data, and enterprise solutions across the United States, is seeking a Reinforcement Learning Engineer for a 100% remote role.

You will design, train, and deploy RL-based systems for high-impact decision-making problems, requiring 8+ years of experience and expertise in RL algorithms, simulation environments, reward modeling, and scaling policies.