Senior Machine Learning Engineer - Forecasting

Amgen • Los Angeles, CA

2d

About The Position

Join Amgen’s Mission of Serving Patients At Amgen, if you feel like you’re part of something bigger, it’s because you are. Our shared mission—to serve patients living with serious illnesses—drives all that we do. Since 1980, we’ve helped pioneer the world of biotech in our fight against the world’s toughest diseases. With our focus on four therapeutic areas –Oncology, Inflammation, General Medicine, and Rare Disease– we reach millions of patients each year. As a member of the Amgen team, you’ll help make a lasting impact on the lives of patients as we research, manufacture, and deliver innovative medicines to help people live longer, fuller happier lives. Our award-winning culture is collaborative, innovative, and science based. If you have a passion for challenges and the opportunities that lay within them, you’ll thrive as part of the Amgen team. Join us and transform the lives of patients while transforming your career. Senior Machine Learning Engineer - Forecasting What You Will Do 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.

Requirements

• Doctorate degree OR Master’s degree and 2 years of applying data science in enterprise environments experience OR Bachelor’s degree and 4 years of applying data science in enterprise environments experience OR Associate’s degree and 8 years of applying data science in enterprise environments experience OR High school diploma / GED and 10 years of applying data science in enterprise environments experience

Nice To Haves

• 6+ 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.
• Strong experience building and maintaining end-to-end ML pipelines and production systems for forecasting or other predictive modeling use cases.
• Expertise in model serving, and operationalizing probabilistic, Bayesian, or predictive models in production environments.
• Strong programming skills in Python and SQL, with experience using tools such as scikit-learn, PyTorch, TensorFlow, and orchestration or workflow tools for ML pipelines.
• Experience with cloud platforms, distributed data processing, containerization, and ML deployment patterns.
• Strong understanding of software engineering fundamentals, including system design, testing, performance optimization, and maintainability.
• Strong collaboration and communication skills, with the ability to work effectively across technical and non-technical teams.
• An intellectually curious self-starter who can take ambiguous problems and build scalable solutions from the ground up.
• Experience building and deploying forecasting models for biotech/pharma use cases with knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, and coverage.
• Experience partnering closely with data scientists to translate advanced statistical or machine learning models into reliable production services.
• Experience leveraging machine learning and forecasting systems in retail, consumer goods, supply chain, or manufacturing applications.
• Familiarity with model monitoring, explainability, and governance requirements in regulated or high-impact business environments.

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.

Benefits

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
• Stock-based long-term incentives
• Award-winning time-off plans
• Flexible work models where possible.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Number of Employees: 5,001-10,000 employees