Reinforcement Learning Engineer

Career Category Engineering Job Description ABOUT AMGEN   Amgen harnesses the best of biology and technology to fight the world’s toughest diseases, and make people’s lives easier, fuller and longer. We discover, develop, manufacture and deliver innovative medicines to help millions of patients. Amgen helped establish the biotechnology industry more than 40 years ago and remains on the cutting-edge of innovation, using technology and human genetic data to push beyond what’s known today.   ABOUT THE ROLE   Role Description:   We are seeking a highly skilled Reinforcement Learning Engineer or Machine Learning Engineer with experience in reinforcement learning (RL) and DevOps/MLOps to join the AI & Data Science team. You will work with data scientists and engineers to build and scale production ML systems using feedback and measured outcomes. You will be expected to own and drive forward scoped projects with technical guidance. The role offers the opportunity to apply reinforcement learning and machine learning methods to practical problems, with appropriate human review and safety controls.  Roles & Responsibilities:    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.  Must-Have Skills:   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).