Description
**Senior Machine Learning Engineer, AI Studio**
**CAREER LEVEL:** GCF 5 – Specialist
**CAREER TRACK:** Individual Contributor
**PRIMARY SCOPE:** End-to-end ownership of a small AI asset or substantial technical workstream
**ORGANIZATION:** Applied AI | AI Studio
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 at the cutting edge of innovation, using technology and human genetic data to push beyond what is known today.
ABOUT THE ROLE
Role Description:
The Senior Machine Learning Engineer position offers a unique opportunity to join a fun, innovative engineering team within the AI & Data Science (AI&D) – organization. We are the Applied AI team (AI Studio). AI Studio is Amgen’s enterprise engine for turning high-value business challenges into scalable AI products. We partner with key business partners across the company to identify the right opportunities, shape them into actionable use cases, and design, build, and launch AI products responsibly. Our work spans the full lifecycle—from early discovery and rapid prototyping to production deployment, reuse across the enterprise, and measurable business impact. You will be part of AI Studio and define and own AI assets or substantial technical workstream from problem framing through architecture, model and system development, evaluation, launch, stabilization, support transition, adoption and measurable outcome.
You will remain hands-on while leading decisions across software, statistics, classical ML, deep learning, NLP, GenAI, RAG, bounded agents, data and knowledge pipelines, APIs, MLOps/LLMOps, security, governance and operations. Within Applied AI – AI Studio turn prioritized business demand into governed, reusable AI assets with accountable ownership and measurable value across software, data, automation, machine learning, Generative AI, RAG, bounded agents, evaluation, observability and lifecycle operations.
Roles & Responsibilities:
+ Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operatingownerand measurable technical and business outcomes.
+ Map rules, exceptions, datadependenciesand human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG,agentsor a manual approach.
+ Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, securityzonesand human review.
+ Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agenttoolsand workflow orchestration.
+ Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expertadjudicationand release thresholds.
+ EstablishMLOps/LLMOpsfor lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity,costand runbooks.
+ Coordinate security, privacy, Responsible AI, Quality, legal, model-risk andGxPcontrols; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices.
Basic Qualifications and Experience:
• Bachelor’s/Master’s degree with 8 – 13 years of experience in Computer Science, IT or related field.
Functional Skills:
+ Advanced software and AI/ML system design: Production Python and SQL, APIs, distributed or event-driven services, data persistence, testing, performance, repository governance, designreviewand end-to-end architecture.
+ Advanced statistics,MLand experimental design: EDA, feature engineering, supervised and unsupervised learning, predictivemodelling, ensembles, anomaly detection, calibration, uncertainty, robustness, explainability and causal reasoning where justified.
+ Deep learning,NLPand foundation models: Selection and production use of neural, transformer, embedding, vision,documentand multimodal approaches, including fine-tuning versus prompting, latency,privacyand cost trade-offs.
+ GenAI, RAG,knowledgeand agentic AI: Grounded retrieval, structured output, provenance, citations, abstention, entitlement controls, bounded tool use, permissions, durable state, recovery, adversarialevaluationand human control.
+ Data, knowledge,cloudand AI operations:Trustworthybatch/streaming pipelines, data contracts, lineage, vector/graph stores, Spark or Databricks, containers, Kubernetes,MLOps/LLMOps, SLOs and lifecycle operations.
+ Responsible AI and regulated delivery: Risk assessment, least privilege, threatmodelling, red teaming, bias and subgroup robustness, privacy, model/data documentation, human accountability, validation and applicableGxPcontrols.
Must-Have Skills:
+ Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome.
+ Strong hands-onproficiencyin Python and SQL, with experience designing production software,servicesand evaluation pipelines.
+ Advancecapability in at least one role-defining pillar—Applied ML, GenAI/RAG/agentsor ML platform/MLOps—plus credible depth across the production lifecycle.
Good-to-Have Skills:
+ Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survivalmethodsor drift-aware retraining.
+ Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT orLoRA, fine-tuning, distillation, quantization, routing,cascadesor inference optimization.
+ Cloud,platformand AI operations: Experience with AWS, Bedrock or SageMaker, Databricks, Spark, Kubernetes, serverless systems, infrastructure as code,MLflow, Airflow, Kubeflow, observability and FinOps.
+ Human-AI and regulated delivery: Experience with review, correction, approval, accessibility, uncertainty communication, workflow automation andGxP-relevant or validated systems.
Soft Skills:
+ Strong product thinking and ability to connect technical decisions to user, workflow, risk,costand business value.
+ Technical leadership, mentoring and constructive challenge whileremaininghands-on.
+ Excellent analytical judgment and clear communication of evidence, uncertainty,trade-offsand limitations.
+ Cross-functional leadership across business, product, architecture,engineeringand control functions.
+ Ownership, resilience and continuous improvement through incidents,feedbackand measured outcomes.
EQUAL OPPORTUNITY STATEMENT
Amgen is an Equal Opportunity employer and will consider you without regard to your race, colour, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
