Remote RL Engineer - Scale Impactful AI/ML Systems

Bright Vision Technologies; Foster City (CA); Remote; USD 80,000 - 100,000; Full time

Job summary

Bright Vision Technologies is seeking a RL Engineer to design, train, and deploy reinforcement-learning systems for high-impact decision tasks. The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and the scale-up of training and evaluation of policies.

The ideal candidate has research depth and pragmatic engineering skills, with a track record of shipping RL work into production where stability, safety, and ongoing improvement matter.

Qualifications

• Master’s or PhD in Computer Science, Machine Learning, or a 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 RL foundations.
• Experience designing and tuning reward functions in non-trivial environments.
• Familiarity with simulation environments and large-scale data 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

Master’s or PhD in Computer Science

Six or more years RL experience

Python proficiency

RL library experience

Probability and optimization

Reward function design

Simulation environments

GPU training of policies

Communication skills

Shipping/publishing RL work

Education

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

Job description

Bright Vision Technologies is seeking a RL Engineer to design, train, and deploy reinforcement-learning systems for high-impact decision tasks. The role requires deep familiarity with modern RL algorithms, simulation environments, reward modeling, and the scale-up of training and evaluation of policies.

The ideal candidate has research depth and pragmatic engineering skills, with a track record of shipping RL work into production where stability, safety, and ongoing improvement matter.