Senior Machine Learning Engineer, AI Studio

About this role

Career Category
EngineeringJob 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.
Senior Machine Learning Engineer, AI Studio
What you will do
Let’s do this. Let’s change the world. In this vital role 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.
Key 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.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The Senior Machine Learning Engineer we seek is a seasoned professional with these qualifications.
Basic Qualifications:
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
Preferred Qualifications:

• 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.
What you can expect from us
As we work to develop treatments that take care of others, we also work to care for your professional and personal growth…