Machine Learning Engineer – RL
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer – RL based in United States.
This role offers the opportunity to build advanced reinforcement learning systems that solve complex decision-making challenges beyond traditional machine learning approaches.
You will design, train, and deploy RL-based models that create measurable impact in real-world applications.
The position combines deep research knowledge with hands-on engineering, requiring the ability to move solutions from experimentation into production.
You will work on scalable training pipelines, simulation environments, reward modeling, and policy optimization.
The role provides exposure to cutting-edge AI techniques, including reinforcement learning from human feedback and large-scale model optimization.
You will collaborate with technical teams to transform innovative ideas into reliable, safe, and high-performing AI systems.
This is an opportunity to contribute to the future of intelligent systems within a remote, innovation-driven environment.
This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Machine Learning Engineer – RL based in United States.
This role offers the opportunity to build advanced reinforcement learning systems that solve complex decision-making challenges beyond traditional machine learning approaches.
You will design, train, and deploy RL-based models that create measurable impact in real-world applications.
The position combines deep research knowledge with hands-on engineering, requiring the ability to move solutions from experimentation into production.
You will work on scalable training pipelines, simulation environments, reward modeling, and policy optimization.
The role provides exposure to cutting-edge AI techniques, including reinforcement learning from human feedback and large-scale model optimization.
You will collaborate with technical teams to transform innovative ideas into reliable, safe, and high-performing AI systems.
This is an opportunity to contribute to the future of intelligent systems within a remote, innovation-driven environment.
Accountabilities:
- Design and implement reinforcement learning solutions for sequential decision-making problems across real and simulated environments.
- Develop, optimize, and maintain simulation environments that support large-scale agent training and evaluation.
- Implement and assess modern RL algorithms, including policy gradient, actor-critic, off-policy, and offline reinforcement learning approaches.
- Design reward functions and shaping strategies that align model behavior with performance goals and safety requirements.
- Apply offline RL, imitation learning, RLHF, DPO, and related techniques where appropriate.
- Build scalable reinforcement learning infrastructure, including distributed training systems, experience collection pipelines, and replay mechanisms.
- Improve training stability, sample efficiency, and overall model performance through algorithmic and engineering enhancements.
- Establish evaluation frameworks, including robustness testing, adversarial scenarios, and out-of-distribution assessments.
- Develop safety mechanisms such as policy constraints, human oversight workflows, and monitoring solutions.
- Collaborate with research, engineering, and product teams to identify and deliver valuable RL applications.
- Monitor deployed models for performance drift, unexpected behavior, and reliability issues.
- Document technical approaches, system architecture, methodologies, and operational considerations.
- Stay informed on advances in reinforcement learning research and apply relevant innovations to production systems.
- Master’s or PhD degree in Computer Science, Machine Learning, Artificial Intelligence, or a related technical field, or equivalent practical 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, platforms, or internal RL systems.
- Strong understanding of probability, optimization methods, and reinforcement learning fundamentals.
- Experience designing and tuning complex reward functions.
- Familiarity with simulation environments, large-scale data collection, and agent training workflows.
- Experience training neural network-based policies using GPU clusters or distributed computing environments.
- Knowledge of reinforcement learning techniques for large language models, including RLHF or related approaches, is a plus.
- Experience with multi-agent reinforcement learning, hierarchical RL, robotics, autonomous systems, or control environments is preferred.
- Strong analytical, problem-solving, documentation, and communication skills.
- Demonstrated ability to deliver impactful reinforcement learning projects through production deployments or research contributions.
- Fully remote position within the United States.
- Full-time direct employment opportunity.
- Competitive annual salary range of $100,000–$150,000.
- Opportunity to work on advanced artificial intelligence and reinforcement learning initiatives.
- Exposure to cutting-edge technologies and complex real-world AI challenges.
- Collaborative environment focused on innovation, technical excellence, and professional growth.
- Career development opportunities within a growing technology-focused organization.
- Eligibility for company-sponsored benefits and employment programs.
The Machine Learning Engineer – RL will be responsible for developing production-ready reinforcement learning solutions, combining algorithmic expertise with strong engineering practices. The role requires ownership of the full lifecycle of RL systems, from research and experimentation to deployment, monitoring, and continuous improvement.
Requirements:
The ideal candidate brings advanced machine learning expertise, strong software engineering capabilities, and experience delivering reinforcement learning systems in practical environments. Candidates should combine theoretical understanding with the ability to build reliable AI solutions at scale.