Sr Machine 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 We are seeking a Senior Machine Learning Engineer, Forecasting to join the Forecasting team within the AI & Data organization. This role will design, build, deploy, and maintain scalable machine learning systems that power forecasting capabilities and uncertainty-aware decision support across the company. This senior member of the team will work cross-functionally to translate advanced forecasting methods into reliable, production-grade solutions that support critical business processes and help Amgen deliver on its “every patient, every time” mandate. The role is particularly well suited to a strong engineer who is excited about building robust ML infrastructure, productionizing state-of-the-art forecasting models, and enabling decision-support solutions that inform multi-horizon planning and business decision-making. Key Responsibilities Design, build, and maintain scalable machine learning systems and forecasting pipelines to support demand forecasting across near-, medium-, and long-term planning horizons. Productionize advanced statistical, Bayesian, and machine learning forecasting models, including training, validation, deployment, and lifecycle management. Build and optimize data pipelines, feature engineering workflows, and batch and real-time inference systems using large, complex datasets. Own the end-to-end ML engineering lifecycle, including solution design, prototyping, model integration, testing, deployment, monitoring, observability, and continuous improvement. Develop robust MLOps capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, drift detection, and rollback strategies. Partner closely with data scientists and business stakeholders to operationalize forecasting, simulation, and scenario-analysis capabilities that support strategic decision-making. Establish and promote software engineering best practices, including code quality, documentation, reproducibility, and system reliability. Research and evaluate emerging tools, platforms, and methodologies in machine learning engineering, forecasting, and AI for potential application to business problems. Basic Qualifications 8+ years of experience in machine learning engineering, software engineering, or a related field, with a demonstrated track record of deploying production ML systems that deliver business value.