Applied RL Engineer

Accountabilities: The Applied RL Engineer will be responsible for designing, implementing, and optimizing reinforcement learning solutions that address complex operational and strategic challenges. This role requires balancing research innovation with production-level engineering standards to deliver scalable and reliable AI systems.

Design, train, evaluate, and deploy reinforcement learning models for complex decision-making problems.

Develop RL systems that move beyond traditional supervised learning approaches to address dynamic and evolving environments.

Apply modern reinforcement learning algorithms, simulation techniques, and reward modeling strategies.

Design and optimize reward functions for challenging, non-trivial environments.

Build and maintain simulation environments and large-scale data collection pipelines for model training.

Train neural network-based policies using GPU clusters and scalable infrastructure.

Evaluate model performance, improve reliability, and support continuous policy optimization after deployment.

Apply engineering best practices to ensure RL systems are stable, safe, and production-ready.

Collaborate with research and engineering teams to translate AI concepts into practical solutions.

Contribute technical insights through documentation, knowledge sharing, research publications, or open-source contributions when applicable.

Requirements:

The ideal candidate will have deep expertise in reinforcement learning, machine learning engineering, and applied AI development. Candidates should demonstrate the ability to combine theoretical knowledge with hands-on experience delivering impactful RL systems.

Master’s degree or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent applied experience.

6+ years of combined reinforcement learning research and engineering experience.

Strong programming skills in Python and experience with modern deep learning frameworks.

Hands-on experience with reinforcement learning libraries or proprietary RL platforms.

Strong understanding of probability, optimization, and reinforcement learning theory.

Experience developing and tuning reward functions in complex environments.

Experience working with simulation environments and large-scale experience collection systems.

Experience training deep learning models using GPU-based infrastructure.

Strong written and verbal communication skills.

Demonstrated track record of delivering impactful reinforcement learning projects or publishing relevant research.

Experience with RLHF for large language models is preferred.

Familiarity with multi-agent reinforcement learning or hierarchical reinforcement learning is a plus.

Exposure to robotics, control systems, autonomous systems, or related fields is advantageous.

Contributions to open-source reinforcement learning libraries or environments are valued.

Benefits:

Fully remote work opportunity within the United States.

Full-time direct employment opportunity.

Competitive annual salary range of $100,000–$150,000 depending on experience and qualifications.

Opportunity to work on advanced artificial intelligence and reinforcement learning initiatives.

Collaborative environment combining research innovation with real-world engineering applications.

Career growth opportunities within a technology-focused organization.

Opportunity to contribute to impactful AI solutions across complex use cases.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether? 

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