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 is known today.
ABOUT THE ROLE
We are seeking a Principal Machine Learning Engineer to join the Forecasting team within the AI & Data Science organization. As a principal technical leader, this role will set technical direction and design, build, deploy, and scale enterprise-grade machine learning, large language model, and agentic AI systems that power forecasting capabilities, uncertainty-aware decision support, scenario planning, and operational decision automation across Amgen.
This role blends deep machine learning engineering expertise, modern AI systems architecture, and product-minded delivery. The Principal Machine Learning Engineer will solve highly ambiguous problems, identify high-impact automation opportunities, and translate advanced forecasting, predictive analytics, LLM-powered applications, and AI agent patterns into reliable, governed production solutions that support critical business processes and help Amgen deliver on its "every patient, every time" mandate.
The role is well suited to a hands-on technical leader who has shipped mission-critical AI/ML systems to production, understands the practical challenges of MLOps, and can operate effectively across engineering, data science, product, operations, commercial, manufacturing, supply chain, and senior stakeholder groups.
ABOUT THE TEAM
The Forecasting team within AI & Data Science is a cross-functional team focused on building AI-native forecasting, simulation, and decision-support capabilities for Amgen. The team partners closely with business, operations, and scientific stakeholders to understand enduring planning challenges, prototype solutions quickly, measure impact rigorously, and deploy reliable systems that inform real business decisions.
Our charter is to identify high-value forecasting and decision automation opportunities, build scalable AI/ML products that can support them reliably, and continuously improve these systems based on real-world performance, user adoption, forecast quality, and measurable business value.
KEY RESPONSIBILITIES
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Define and drive the technical strategy for enterprise forecasting and AI decision systems, aligning architecture, reusable platforms, and delivery roadmaps to Amgen's planning, supply, commercial, manufacturing, operations, and patient-focused priorities.
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Partner with data scientists, product and program leaders, operations, commercial, manufacturing, supply chain, finance, and other business stakeholders to translate ambiguous requirements into shipped software and measurable business outcomes.
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Architect, build, and scale production ML, LLM, and agentic AI systems that combine forecasting, predictive analytics, simulation, optimization, and autonomous or semi-autonomous workflow automation.
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Productionize advanced statistical, Bayesian, deep learning, and machine learning models, including training, validation, inference, serving, evaluation, lifecycle management, and governed deployment.
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Lead development of AI agent components that automate complex forecasting and operational workflows across multiple systems, decision points, datasets, and user groups while preserving appropriate human-in-the-loop review and escalation patterns.
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Design secure integrations across enterprise APIs, databases, analytics platforms, workflow systems, cloud services, and AI orchestration patterns to enable multi-system decision support and scalable automation.
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Establish robust MLOps and AI engineering capabilities, including model versioning, CI/CD, automated retraining, performance monitoring, observability, drift detection, service-level reliability, rollback strategies, and operational runbooks.
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Implement guardrails, model and agent evaluation frameworks, auditability, explainability, responsible AI controls, and human-in-the-loop operating models for production AI systems in high-impact and regulated business contexts.
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Research and evaluate state-of-the-art open-source, vendor, and internal tools related to forecasting, LLMs, AI agents, MLOps, model optimization, model serving, and scalable AI infrastructure for potential application to Amgen business problems.
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Provide principal-level technical mentorship, design review leadership, and engineering standard-setting across teams, promoting code quality, documentation, reproducibility, testing, security, privacy, maintainability, and operational excellence.
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Influence AI & Data Science technical and product roadmaps by identifying reusable patterns, platform opportunities, and enterprise-wide automation opportunities that accelerate Amgen's ability to serve patients.
BASIC QUALIFICATIONS
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Degree and 12+ years of experience in machine learning engineering, software engineering, data science engineering, or a related quantitative discipline.
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10+ years of professional experience building, deploying, and operating production ML, AI, data, or software systems, including significant experience as a technical lead on complex, cross-functional initiatives.
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Demonstrated track record of designing or architecting new and existing systems with emphasis on reliability, scale, security, maintainability, and operational excellence.
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Deep hands-on experience with the full ML engineering lifecycle, including data pipelines, feature engineering, experimentation, model training, model integration, testing, deployment, monitoring, evaluation, observability, and continuous improvement.
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Strong experience deploying forecasting, probabilistic, Bayesian, predictive, NLP, deep learning, or LLM-based systems in production environments.
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Experience building or integrating AI systems, including LLM-powered applications, agentic workflows, retrieval or information-retrieval systems, evaluation frameworks, and human-in-the-loop review patterns.
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Strong object-oriented programming skills in Python and SQL, with experience using modern ML and software development frameworks such as scikit-learn, PyTorch, TensorFlow/JAX, Spark, Ray, MLflow, Airflow/Prefect/Dagster, FastAPI, or equivalent technologies.
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Experience with cloud platforms and distributed systems, including containerization, CI/CD, infrastructure-as-code, model serving, workflow orchestration, batch and streaming data processing, and production support.
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Strong software engineering fundamentals, including system design, architecture trade-off analysis, testing strategies, code reviews, source control, build and release processes, performance optimization, and maintainability.
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Demonstrated ability to communicate technical strategy, system tradeoffs, and delivery risks to technical and non-technical stakeholders, including senior leaders, product/program owners, scientists, and business partners.
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Demonstrated ability to lead through ambiguity, define technical direction, mentor others, and deliver measurable business value in a matrixed enterprise environment.
PREFERRED QUALIFICATIONS
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Experience building and deploying forecasting, demand planning, supply chain, manufacturing, commercial analytics, or operations decision-support systems in biotech, pharma, healthcare, retail, consumer goods, or other complex regulated or operational environments.
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Knowledge of healthcare commercial concepts such as payer/provider dynamics, formulary access, coverage, patient access, channel dynamics, and product lifecycle considerations.
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Experience with AI agent architectures, multi-system orchestration, tool/function calling, retrieval-augmented generation, MCP or similar integration patterns, and LLM serving or optimization platforms such as vLLM, SGLang, TensorRT-LLM, or equivalent production inference technologies.
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Experience building internal front-end applications, dashboards, workflow tools, or decision-support products that enable non-technical users to understand, trust, and act on AI outputs.
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Experience designing guardrails, model/agent evaluation suites, A/B tests, offline and online metrics, auditability, explainability, fairness, risk management, and governance controls for high-impact AI/ML systems.
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Experience influencing enterprise technical standards, architecture roadmaps, reusable platform capabilities, or engineering communities of practice.
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Publications, patents, open-source contributions, conference presentations, or other evidence of thought leadership in machine learning engineering, forecasting, LLMs, AI systems, MLOps, or decision intelligence.
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, or disability status.
We will ensure that individuals with disabilities are provided with reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment. Please contact us to request an accommodation.
