Job description

Career Category Information Systems Job Description 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. Amgen is advancing a broad and deep pipeline of medicines to treat cancer, heart disease, inflammatory conditions, rare diseases, and obesity and obesity-related conditions. 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. Principal Machine Learning Engineer What you will do Let’s do this. Let’s change the world. In this vital role you will Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem. We are seeking a Principal Machine Learning Engineer —Amgen’s most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI solutions. Sitting at the intersection of engineering excellence and data-science enablement, you will develop, deploy and monitor models—classical ML, deep learning and LLMs—securely and cost-effectively. Acting as a “player-coach,” you will establish AI solution strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI solutions. Roles & Responsibilities: Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem. Build production ML/GenAI solutions and lightweight apps delivering sub-second insights. Build end-to-end ML pipelines —data ingestion, feature engineering, training, hyper-parameter optimisation, evaluation, registration and automated promotion—using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks. Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency. Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks. Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining. Architect LLM/RAG with prompt management, safety guardrails, and optimized inference. Enforce data quality , lineage, and model/data cards; apply privacy-preserving techniques where needed. Contribute reusable ML/GenAI components —feature stores, model registries, experiment-tracking libraries—and evangelize best practices that raise engineering velocity across squads. Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness. Prototype and benchmark new algorithms , offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs. Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs. What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Principal Machine Learning Engineer with these qualifications. Basic Qualifications: Doctorate degree and 2 years of Machine Learning Engineer experience OR Master’s degree and 6 years of Machine Learning Engineer experience OR Bachelor’s degree and 8 years of Machine Learning Engineer experience OR Associate’s degree and 10 years of Machine Learning Engineer experience OR High school diploma / GED and 12 years of Machine Learning Engineer experience In addition to meeting at least one of the above requirements, you must have a minimum of 2 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above 3-5 years in AI/ML and enterprise software. Strong command of machine-learning algorithms — regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques—with the judgment to choose, tune and operationalize the right method for a given business problem. Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale. Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, LangGraph, Semantic Kernel). Proficiency in Python and Java; containerization (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines). Strong business-case skills—able to model TCO vs. NPV and present trade-offs to executives. Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives. Preferred Qualifications: Experience in Biotechnology or pharma industry is a big plus Published thought-leadership or conference talks on enterprise GenAI adoption. Master’s degree in Computer Science and or Data Science Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery. Education and Professional Certifications Master’s degree with 10-12 years of experience in Computer Science, IT or related field OR Bachelor’s degree with 12-14 years of experience in Computer Science, IT or related field Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus. Soft Skills: Excellent analytical and troubleshooting skills. Strong verbal and written communication skills Ability to work effectively with global, virtual teams High degree of initiative and self-motivation. Ability to manage multiple priorities successfully. Team-oriented, with a focus on achieving team goals. Ability to learn quickly, be organized and detail oriented. Strong presentation and public speaking skills. What you can expect of us As we work to develop treatments that take care of others, we also work to care for your professional and personal growth and well-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way. The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications. In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include: 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. Refer to the Work Location Type in the job posting to see if this applies. Apply now and make a lasting impact with the Amgen team. careers.amgen.com In any materials you submit, you may redact or remove age-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information. Application deadline Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position. Sponsorship Sponsorship for this role is not guaranteed. As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease. Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law. We will ensure that individuals with disabilities are provided 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 accommodation. . Salary Range 187,395.25USD -253,534.75 USD

Requirements

Must-haves

Principal Machine Learning Engineer
What you will do Let’s do this. Let’s change the world. In this vital role you will Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem.
Own enterprise AI/ML architecture, standards, APIs, and guardrails across cloud/on-prem.
Build production ML/GenAI solutions and lightweight apps delivering sub-second insights.
Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
Establish observability, SLOs, and safe deploys (blue-green/canary, shadow, rollbacks) with incident runbooks.
Lead rigorous evaluation (offline/online, A/B), drift detection, and automated retraining.
Architect LLM/RAG with prompt management, safety guardrails, and optimized inference.
Enforce data quality , lineage, and model/data cards; apply privacy-preserving techniques where needed.
Contribute reusable ML/GenAI components —feature stores, model registries, experiment-tracking libraries—and evangelize best practices that raise engineering velocity across squads.
Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
Prototype and benchmark new algorithms , offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.
Translate domain needs (R&D, Manufacturing, Commercial) into roadmaps; mentor teams and communicate trade-offs.
What we expect of you We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a Principal Machine Learning Engineer with these qualifications.
Doctorate degree and 2 years of Machine Learning Engineer experience
Master’s degree and 6 years of Machine Learning Engineer experience
Bachelor’s degree and 8 years of Machine Learning Engineer experience

Nice-to-haves

Experience in Biotechnology or pharma industry is a big plus
Published thought-leadership or conference talks on enterprise GenAI adoption.
Master’s degree in Computer Science and or Data Science
Familiarity with Agile methodologies and Scaled Agile Framework (SAFe) for project delivery.
Education and Professional Certifications
Certifications on GenAI/ML platforms (AWS AI, Azure AI Engineer, Google Cloud ML, etc.) are a plus.
A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaimlllmpythonjavadockerawsazuregcpagile

Platform: workday
Effort: long

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