Sr Associate Data Scientist

ABOUT THE ROLE

The 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.

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.

Basic Qualifications

• BS or MS in Computer Science, Engineering, Computational Biology, Bioinformatics, or related field
• Strong hands-on experience developing AI and machine learning solutions
• Expertise in Python and modern AI/ML development frameworks
• Experience designing and implementing production-quality software systems

Preferred Qualifications

• Experience with LLMs, agentic AI systems, and workflow orchestration
• Experience with RAG, vector databases, and knowledge-driven AI architectures
• Experience integrating scientific or domain-specific AI models
• Familiarity with biological, biomedical, or life sciences data
• Experience with cloud AI platforms (AWS Bedrock, SageMaker, Azure AI, or equivalent)
• Familiarity with knowledge graphs, Graph-RAG, or scientific knowledge systems
• Experience working closely with researchers and domain experts.

Core Competencies

Strong 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 databases
• API-driven architectures
• Python-based AI and ML ecosystems

Understanding of:

• Machine learning lifecycle and evaluation
• Scientific computing workflows
• Distributed systems and scalable architectures
• Knowledge graph concepts and graph-based AI approaches

Ability to operate effectively in highly collaborative, cross-functional scientific environments.

Preferred Experience:

• Bachelor's with 5–9 years of experience.

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 accommodation.

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Career Category

Research

Agentic AI Systems Development

Design and implement agent-based systems that support complex scientific workflows.

Develop capabilities including:

• Tool calling and tool orchestration
• Multi-step reasoning workflows
• Retrieval-augmented generation (RAG)
• Knowledge-grounded AI systems
• Human-in-the-loop decision workflows
• Multi-agent collaboration patterns

Build reusable components for:

• Agent orchestration
• Context management
• Memory and state handling
• Workflow planning and execution
• Scientific tool integration

Evaluate emerging agent frameworks and contribute to standards and best practices across projects.

Scientific AI & Model Integration

Integrate foundation models and scientific AI models into end-to-end workflows.

Examples may include:

• Protein language models
• Structure prediction models
• Biological foundation models
• Knowledge graph-based systems
• Predictive machine learning models

Develop 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 & Retrieval

Design and implement knowledge-driven AI systems that connect LLMs and agents with enterprise and scientific data.

Develop solutions utilizing:

• Retrieval-augmented generation (RAG)
• Vector databases
• Knowledge graphs
• Graph-RAG architectures
• Scientific literature and domain knowledge repositories

Ensure AI systems leverage authoritative knowledge sources and support traceability and explainability.

AI Workflow Engineering

Develop end-to-end workflows that combine:

• Data ingestion and preparation
• Knowledge retrieval
• Model inference
• Agent orchestration
• Scientific analysis

Create 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 AI

Develop evaluation frameworks for AI systems, agents, and workflows.

Establish approaches for measuring:

• Accuracy
• Reliability
• Scientific relevance
• Hallucination rates
• Workflow effectiveness
• User adoption and impact

Support responsible AI practices including transparency, traceability, and governance requirements.

Collaboration & Scientific Partnership

Partner closely with:

• AI domain leads
• Scientists and researchers
• Data engineering teams
• Platform engineering teams
• Enterprise AI platform teams

Translate 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 Success Measures

• Delivery of reusable AI capabilities and agentic workflows
• Adoption of AI solutions by scientific teams
• Quality and reliability of deployed AI systems
• Reduction of manual effort through workflow automation
• Reusability of components across multiple scientific domains
• Effective collaboration with scientific and engineering stakeholders

Key Relationships

Works closely with:

• GCF6 Scientific AI Leads
• Scientists and domain experts
• Data Engineering teams
• Enterprise AI Platform teams
• Infrastructure and production engineering organizations

Decision Authority

Makes implementation decisions regarding:

• Agent architectures
• Workflow composition
• Knowledge retrieval strategies
• Model integration approaches
• Evaluation methodologies

Influences broader architectural direction through technical expertise and collaboration with senior technical leaders.