Remote Applied RL Engineer — Scale & Deploy ML Policies

Bright Vision Technologies; United States; Remote; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Applied RL Engineer for 100% remote work in the United States. The role focuses on designing, training, and deploying reinforcement learning systems for real-world decision tasks, with emphasis on production stability, safety, and scalability.

Candidates should have 6+ years of RL research and engineering, strong Python and DL frameworks, and a track record of shipping RL work. Sponsorship: U.S.

Qualifications

• Master's or PhD in Computer Science, ML, or related field, or equivalent experience.
• Six+ years in RL research and engineering.
• Strong Python and DL framework proficiency.
• Experience with RL libraries and tooling.

Responsibilities

• Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
• Develop, calibrate, and maintain simulation environments for large-scale agent training.
• Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
• Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
• Apply offline RL and imitation learning techniques where exploration is costly or unsafe.

Skills

Python programming

RL research

Deep learning

Communication skills

RL deployment

Education

Master’s or PhD in CS/ML

Tools

PyTorch

TensorFlow

Job description

Bright Vision Technologies is seeking an Applied RL Engineer for 100% remote work in the United States. The role focuses on designing, training, and deploying reinforcement learning systems for real-world decision tasks, with emphasis on production stability, safety, and scalability.

Candidates should have 6+ years of RL research and engineering, strong Python and DL frameworks, and a track record of shipping RL work. Sponsorship: U.S.