Remote RL ML Engineer — Scale and Deploy High-Impact Policies

Bright Vision Technologies; Wildwood (MO); Remote; USD 155,000 - 180,000; Full time

Job summary

Bright Vision Technologies is seeking a Machine Learning Engineer – RL 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 reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale.

The ideal candidate has both research depth and engineering pragmatism, with experience

Qualifications

• Master’s or PhD in Computer Science, ML, or related field, or equivalent experience.
• Six+ years of RL research and engineering experience.
• Strong proficiency in Python and modern DL frameworks.
• Hands-on experience with major RL libraries or in-house stacks.
• Solid foundations in probability and optimization for RL.
• Experience designing reward functions for complex environments.
• Experience with simulation environments and large-scale data collection.
• Experience training neural policies on GPUs.
• Strong written and verbal communication skills.
• Track record of shipping or publishing RL work.

Responsibilities

• Design, train, and deploy RL-based systems for decision-making.
• Take RL solutions from lab to production with a focus on stability and safety.
• Collaborate with research and engineering to improve models and systems.
• Present findings and communicate effectively with stakeholders.

Skills

Python

Reinforcement Learning

GPU training

RL libraries

Probability & Optimization

Simulation environments

Education

Masters or PhD in CS/ML

Tools

PyTorch

TensorFlow

OpenAI Gym

RLlib

Job description

Bright Vision Technologies is seeking a Machine Learning Engineer – RL 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 reinforcement learning algorithms, simulation environments, reward modeling, and the engineering complexity of training and evaluating policies at scale.

The ideal candidate has both research depth and engineering pragmatism, with experience