Remote Reinforcement Learning Engineer — Scale & Deploy

Bright Vision Technologies; Reston (VA); On-site; USD 100,000 - 150,000; Full time

Job summary

Bright Vision Technologies is seeking an Applied Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient.

The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale, with a focus on production readiness and safety.

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.

Skills

Reinforcement learning

Python

Deep learning frameworks

RL libraries

GPU training

Probability & optimization

Simulation environments

Research to production

Communication skills

Shipping RL work

Education

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

Tools

PyTorch

TensorFlow

CUDA / GPU clusters

Job description

Bright Vision Technologies is seeking an Applied Reinforcement Learning Engineer to design, train, and deploy RL-based systems for high-impact decision-making problems where supervised learning alone is insufficient.

The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale, with a focus on production readiness and safety.