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# Reinforcement Learning Engineer

- **Company:** Bright Vision Technologies
- **Location:** Monroeville, PA, United States (Remote available)
- **Experience:** Expert
- **Salary:** $100,000.0 - $150,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Neural Networks, Computer Clusters, Python (Programming Language), Machine Learning, Reinforcement Learning, Large Language Models, Multi-Agent Systems, Deep Learning, Information Technology, Free and Open-Source Software
- **Published:** September 26, 2026
- **Apply:** https://www.careerjet.com/jobad/us3e4ec027b08722726719cde99aab392d

## About the Role

Sponsorship: U.S. Citizens, Green Card Holders, EAD Holders, and H-1B transfer candidates are encouraged to apply. We are unable to sponsor new H-1B visa petitions for this position., * 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

* Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
 * Develop, calibrate, and maintain simulation environments suitable for large-scale agent training.
 * Implement and evaluate modern RL algorithms including policy gradient, actor-critic, off-policy, and offline RL methods.
 * Engineer reward functions and shaping strategies that align agent behavior with desired outcomes and safety constraints.
 * Apply offline RL and imitation learning techniques where exploration is costly or unsafe.
 * Use RLHF, DPO, and related techniques for fine-tuning large language models when relevant.
 * Build scalable training infrastructure for distributed RL, including efficient experience collection and replay systems.
 * Optimize training stability and sample efficiency through algorithmic and engineering improvements.
 * Design rigorous evaluation protocols, including out-of-distribution and adversarial test cases.
 * Implement safety mechanisms such as constraint enforcement, conservative policies, and human-in-the-loop oversight.
 * Collaborate with applied scientists and product teams to identify high-value RL use cases.
 * Monitor deployed policies and models in production for drift, regression, and unintended behaviors, building the alerting and dashboards that surface issues before they meaningfully affect users.
 * Document methodology, design decisions, and operational characteristics for internal stakeholders.
 * Stay current with RL research and translate promising techniques into production-ready solutions.