Reinforcement Learning Engineer

Bright Vision Technologies•North Brunswick, NJ

14h•$100,000 - $150,000•Remote

About The Position

Bright Vision Technologies is hiring a full-time Reinforcement Learning Engineer for a 100% remote role in the United States. The engineer will design, implement, evaluate, and deploy reinforcement learning solutions for sequential decision-making in real and simulated environments. The role involves developing simulation environments and scalable distributed training infrastructure, applying modern RL methods, improving training stability and sample efficiency, and implementing safety and evaluation mechanisms. The engineer will collaborate with applied scientists and product teams, monitor deployed policies and models, document technical decisions, and translate relevant research into production-ready solutions. The position requires 10 or more years of combined reinforcement learning research and engineering experience, along with a master’s degree or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience. The stated annual salary range is $100,000–$150,000. The role is direct W2, and the employer does not sponsor new H-1B petitions; U.S. citizens, Green Card holders, EAD holders, and H-1B transfer candidates are encouraged to apply.

Requirements

Master’s or PhD in Computer Science, Machine Learning, or a related field, or equivalent applied experience.
Ten or more years of combined reinforcement learning 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 reinforcement learning.
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 reinforcement learning work.

Nice To Haves

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.

Responsibilities

Design and implement reinforcement learning solutions for sequential decision-making problems in real and simulated environments.
Develop, calibrate, and maintain simulation environments 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 when exploration is costly or unsafe.
Use RLHF, DPO, and related techniques to fine-tune large language models when relevant.
Build scalable infrastructure for distributed RL, including efficient experience collection and replay systems.
Improve training stability and sample efficiency through algorithmic and engineering improvements.
Design evaluation protocols that include 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 production policies and models for drift, regression, and unintended behaviors, and build alerting and dashboards to surface issues.
Document methodology, design decisions, and operational characteristics for internal stakeholders.
Stay current with RL research and translate promising techniques into production-ready solutions.