Reinforcement Learning Engineer

Bright Vision Technologies

Reinforcement Learning Engineer designing and implementing scalable RL solutions for business processes. Collaborating with teams to deliver impactful technology-driven outcomes in a remote setting.

Posted 5/17/2026full-timeRemote • California • 🇺🇸 United StatesMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

Python

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Comprehensive benefits
Competitive compensation packages
Supportive work-life balance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

reinforcement learningpolicy gradientactor-criticoff-policy RLoffline RLimitation learningreward functionsPythondeep learning frameworksneural network training

Soft Skills

strong written communicationstrong verbal communicationcollaborationproblem-solvingdocumentation

Certifications

Master’s in Computer SciencePhD in Machine Learning