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# Machine Learning Engineer - RL

- **Company:** Bright Vision Technologies
- **Location:** Bedford, TX, United States (Remote available)
- **Experience:** Expert
- **Salary:** $155,000.0 - $180,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Computer Clusters, Python (Programming Language), Machine Learning, Reinforcement Learning, Supervised Learning, Large Language Models, Multi-Agent Systems, Deep Learning, Information Technology, Free and Open-Source Software
- **Published:** August 23, 2026
- **Apply:** https://www.careerjet.com/jobad/us9a3945ef580f41e73ed64b31b177112f

## About the Role

* 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.

Preferred Qualifications

 * Experience with RLHF for large language models.
 * Familiarity with multi-agent RL or hierarchical RL.
 * Exposure to robotics, control systems, or autonomous driving.
 * Publications in RL or related research venues.
 * Open-source contributions to RL libraries or environments.

## Description

We are looking for 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 taking RL solutions out of the lab and into production where stability, safety, and ongoing improvement are critical.


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