Senior Machine Learning Engineer, AI Studio

Amgen is a biotechnology company focused on serving patients with serious illnesses. The Senior Machine Learning Engineer will be part of AI Studio, responsible for defining and owning AI assets and leading technical workstreams from problem framing to measurable outcomes.

Responsibilities:

• Define the user, workflow, decision, intended use, baseline, value hypothesis, acceptance criteria, adoption path, operating owner and measurable technical and business outcomes
• Map rules, exceptions, data dependencies and human decision points before selecting deterministic automation, classical ML, deep learning, GenAI, RAG, agents or a manual approach
• Own production architecture across data, feature and knowledge pipelines, models, retrieval, agents, APIs, persistence, workflows, user experience, security zones and human review
• Lead hands-on development of production software, EDA, feature engineering, predictive models, deep-learning or NLP components, inference services, RAG, agent tools and workflow orchestration
• Establish baselines, experiment design, leakage controls, uncertainty, calibration, subgroup and robustness checks, gold sets, error taxonomies, expert adjudication and release thresholds
• Establish MLOps/LLMOps for lineage, reproducibility, versioning, CI/CD, canary or shadow release, observability, drift monitoring, SLOs, rollback, incidents, disaster recovery, capacity, cost and runbooks
• Coordinate security, privacy, Responsible AI, Quality, legal, model-risk and GxP controls; create reusable capabilities, measure adoption and value, mentor engineers and improve delivery practices

Requirements:

• Doctorate degree
• OR Master's degree and 2 years of experience in Computer Science, IT or related fields
• OR Bachelor's degree and 4 years of experience in Computer Science, IT or related fields
• OR Associate's degree and 8 years of experience in Computer Science, IT or related fields
• OR High school diploma / GED and 10 years of experience in Computer Science, IT or related fields
• Demonstrated end-to-end ownership of at least one production ML, GenAI, software, data or automation system that delivered a measurable outcome
• Strong hands-on proficiency in Python and SQL, with experience designing production software, services and evaluation pipelines
• Advance capability in at least one role-defining pillar—Applied ML, GenAI/RAG/agents or ML platform/MLOps—plus credible depth across the production lifecycle
• Advanced ML, causal and uncertainty methods: Experience with data-centric AI, weak supervision, active learning, conformal or Bayesian uncertainty, causal inference, time-series, survival methods or drift-aware retraining
• Advanced deep learning and model efficiency: Experience with transformers, multimodal pipelines, CNNs, RNNs, GNNs, PEFT or LoRA, fine-tuning, distillation, quantization, routing, cascades or inference optimization
• Cloud, platform and 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 and GxP-relevant or validated systems
• Strong product thinking and ability to connect technical decisions to user, workflow, risk, cost and business value
• Technical leadership, mentoring and constructive challenge while remaining hands-on
• Excellent analytical judgment and clear communication of evidence, uncertainty, trade-offs and limitations
• Cross-functional leadership across business, product, architecture, engineering and control functions
• Ownership, resilience and continuous improvement through incidents, feedback and measured outcomes