Senior Machine Learning Engineer

Role Description

Join Amgen’s Mission of Serving Patients. 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
• 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.

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

Requirements

• 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.

Benefits

• A comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions, group medical, dental and vision coverage, life and disability insurance, and flexible spending accounts.
• A discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan.
• Stock-based long-term incentives.
• Award-winning time-off plans.
• Flexible work models where possible.

Salary Range

The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is 156,190.05 USD - 211,315.95 USD. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.