Career CategoryResearchJob DescriptionJoin Amgen’s Mission of Serving Patients ABOUT AMGENAmgen 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 ROLEThe GCF4 Senior ML Engineer – Agentic AI & Scientific Systems is a senior technical contributor responsible for designing, building, and integrating AI capabilities that accelerate scientific discovery across domains such as protein engineering, structure prediction, disease biology, and target identification.This role focuses on developing agentic AI systems and scientific AI workflows that combine foundation models, domain-specific models, knowledge sources, and computational tools into reusable solutions that support scientific decision-making.The engineer works closely with scientific domain leads to translate research needs into scalable AI solutions and reusable capabilities. This role serves as a bridge between scientific innovation and enterprise AI platforms, helping establish a foundation for next-generation AI-assisted scientific workflows.Core ResponsibilitiesAgentic AI Systems DevelopmentDesign and implement agent-based systems that support complex scientific workflows.Develop capabilities including:Tool calling and tool orchestrationMulti-step reasoning workflowsRetrieval-augmented generation (RAG)Knowledge-grounded AI systemsHuman-in-the-loop decision workflowsMulti-agent collaboration patternsBuild reusable components for:Agent orchestrationContext managementMemory and state handlingWorkflow planning and executionScientific tool integrationEvaluate emerging agent frameworks and contribute to standards and best practices across projects.Scientific AI & Model IntegrationIntegrate foundation models and scientific AI models into end-to-end workflows.Examples may include:Protein language modelsStructure prediction modelsBiological foundation modelsKnowledge graph-based systemsPredictive machine learning modelsDevelop reusable APIs, services, and interfaces that allow AI agents and applications to consume scientific models and computational tools.Collaborate with scientific domain experts to identify appropriate modeling approaches and evaluate solution effectiveness.Knowledge Systems & RetrievalDesign and implement knowledge-driven AI systems that connect LLMs and agents with enterprise and scientific data.Develop solutions utilizing:Retrieval-augmented generation (RAG)Vector databasesKnowledge graphsGraph-RAG architecturesScientific literature and domain knowledge repositoriesEnsure AI systems leverage authoritative knowledge sources and support traceability and explainability.AI Workflow EngineeringDevelop end-to-end workflows that combine:Data ingestion and preparationKnowledge retrievalModel inferenceAgent orchestrationScientific analysisCreate reusable workflow patterns that can be applied across multiple scientific domains and projects.Contribute to architectural decisions regarding workflow design, model integration, and AI system composition.Evaluation & Responsible AIDevelop evaluation frameworks for AI systems, agents, and workflows.Establish approaches for measuring:AccuracyReliabilityScientific relevanceHallucination ratesWorkflow effectivenessUser adoption and impactSupport responsible AI practices including transparency, traceability, and governance requirements.Collaboration & Scientific PartnershipPartner closely with:AI domain leadsScientists and researchersData engineering teamsPlatform engineering teamsEnterprise AI platform teamsTranslate scientific requirements into technical solutions and provide guidance on AI capabilities, limitations, and implementation approaches.Contribute to technical design reviews and mentor junior team members where appropriate.Core CompetenciesStrong engineering background in AI and machine learning systems.Hands-on experience with:Large Language Models (LLMs)Agent frameworks (LangGraph, LangChain, AutoGen, CrewAI, Semantic Kernel, or similar)Retrieval-Augmented Generation (RAG)Vector databasesAPI-driven architecturesPython-based AI and ML ecosystemsUnderstanding of:Machine learning lifecycle and evaluationScientific computing workflowsDistributed systems and scalable architecturesKnowledge graph concepts and graph-based AI approachesAbility to operate effectively in highly collaborative, cross-functional scientific environments.Core Success MeasuresDelivery of reusable AI capabilities and agentic workflowsAdoption of AI solutions by scientific teamsQuality and reliability of deployed AI systemsReduction of manual effort through workflow automationReusability of components across multiple scientific domainsEffective collaboration with scientific and engineering stakeholdersKey RelationshipsWorks closely with:GCF6 Scientific AI LeadsScientists and domain expertsData Engineering teamsEnterprise AI Platform teamsInfrastructure and production engineering organizationsDecision AuthorityMakes implementation decisions regarding:Agent architecturesWorkflow compositionKnowledge retrieval strategiesModel integration approachesEvaluation methodologiesInfluences broader architectural direction through technical expertise and collaboration with senior technical leaders.QualificationsBasic QualificationsBS or MS in Computer Science, Engineering, Computational Biology, Bioinformatics, or related fieldStrong hands-on experience developing AI and machine learning solutionsExpertise in Python and modern AI/ML development frameworksExperience designing and implementing production-quality software systemsPreferred QualificationsExperience with LLMs, agentic AI systems, and workflow orchestrationExperience with RAG, vector databases, and knowledge-driven AI architecturesExperience integrating scientific or domain-specific AI modelsFamiliarity with biological, biomedical, or life sciences dataExperience with cloud AI platforms (AWS Bedrock, SageMaker, Azure AI, or equivalent)Familiarity with knowledge graphs, Graph-RAG, or scientific knowledge systemsExperience working closely with researchers and domain experts.Preferred Experience:Bachelor's with 5–9 years of experience.EQUAL OPPORTUNITY STATEMENTAmgen 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 accommodation..