Reinforcement Learning Engineer

Overview

Build and scale reinforcement learning and machine learning systems that learn from feedback and measured outcomes, working with data scientists and engineers to take them from prototype to production.

What you'll do

• Partner with data scientists and software engineers to frame business problems as reinforcement learning and identify suitable approaches.
• Implement and evaluate reinforcement learning methods, such as contextual bandits, policy learning, or preference-based approaches, with technical guidance, for applications that learn from feedback.
• Build and scale Python-based ML systems from prototypes to production using frameworks such as PyTorch, scikit-learn, or other relevant machine learning libraries.
• Design evaluation methods and analyze results using appropriate metrics, baselines, statistical methods, and offline or online experiments.
• Work with human and AI-generated feedback, evaluation data, and interaction traces to support model improvement and learning from preferences.
• Implement DevOps/MLOps best practices to build reliable, reproducible ML workflows, including experiment tracking, data and model versioning, testing, and deployment.
• Collaborate with product and platform teams to integrate models into AI applications and monitor their performance after deployment.
• Document technical work, communicate findings, and keep current with relevant developments in reinforcement learning and machine learning.

What you'll need

• Solid foundation in machine learning algorithms and techniques.
• Experience in MLOps practices and tools (e.g., MLflow, Kubeflow, Airflow).
• Experience in DevOps tools (e.g., Docker, Kubernetes, CI/CD), including hands-on experience building and deploying ML systems.
• Hands-on experience with RL concepts such as rewards, policies, value functions, explore-exploit tradeoff, and RL with human feedback.
• Proficiency in Python and relevant ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn).
• Ability to design experiments, analyze model behavior, and explain technical results clearly; strong problem-solving and collaboration skills.
• Ability to learn quickly.
• Excellent communication and interpersonal skills.
• Master's degree with 5+ years of experience in Computer Science, IT or related field, OR Bachelor's degree with 7-9+ years of experience in Computer Science, IT or related field.
• Excellent verbal and written communication skills.
• Ability to work effectively with cross-functional and global teams.
• Ability to own and drive scoped projects with technical guidance and regular feedback.
• Curiosity, initiative, and willingness to learn from feedback.
• Ability to organize work and manage multiple priorities.
• Collaborative approach and focus on shared team goals.
• Attention to detail when handling data, experiments, and technical documentation.
• Ability to present findings to technical and nontechnical audiences.
• Strong analytical, debugging, and problem-solving skills.

Nice to have

• Familiarity with RL environments and libraries such as Gymnasium, Stable-Baselines3, Ray RLlib, TorchRL, or Vowpal Wabbit.
• Experience with big data technologies (e.g., Spark, Hadoop), and performance tuning in query and data processing.
• Experience with data engineering and pipeline development.
• Familiarity with large language models, natural language processing, AI agents, or evaluation of generative AI systems.