Senior RL Engineer — Remote, Scale RL Systems

Bright Vision Technologies; Artesia, Cerritos (CA, CA); Remote; USD 80.000 - 100.000; Vollzeit

Zusammenfassung

Bright Vision Technologies is seeking an experienced RL Engineer to design, train, and deploy reinforcement learning systems for high-impact decision-making tasks. The role emphasizes solving problems where supervised learning falls short, and requires production-grade experience with RL methods, simulations, reward modeling, and scalable training.

The successful candidate will have a strong research background and pragmatic engineering skills, with a track record shipping RL work and

Qualifikationen

• 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.

Aufgaben

• Design, train, and deploy RL-based systems for high-impact decision problems.
• Work with simulations, reward modeling, and scalable training pipelines.
• Translate RL research into production-ready solutions with stability and safety in mind.

Kenntnisse

RL algorithms

Python

Deep learning

RL library experience

Probability & optimization

Reward design

Simulation environments

GPU training

Communication

Research to production

Ausbildung

Master's or PhD in CS/ML

Equivalent applied experience

Tools

PyTorch

TensorFlow

Jobbeschreibung

Bright Vision Technologies is seeking an experienced RL Engineer to design, train, and deploy reinforcement learning systems for high-impact decision-making tasks. The role emphasizes solving problems where supervised learning falls short, and requires production-grade experience with RL methods, simulations, reward modeling, and scalable training.

The successful candidate will have a strong research background and pragmatic engineering skills, with a track record shipping RL work and