Interested in this AI/ML Engineer role at Amgen?
Apply Now →Skills & Technologies
About This Role
United States \- Remote
JOB ID: R\-250803 LOCATION: United States \- Remote WORK LOCATION TYPE: Remote DATE POSTED: Aug. 03, 2026 CATEGORY: Information Systems SALARY RANGE: 110,038\.45USD \-148,875\.55 USD
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.
Digital AI Product Analyst
What you will do
Let’s do this. Let’s change the world. The Digital AI Product Analyst, AI Studio position offers a unique opportunity to join a fun, innovative team within the AI \& Data Science organization. This role will support next\-generation AI, automation, process intelligence, digital workflow, and productivity capabilities across Amgen’s AI Studio portfolio.
The job 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.
AI Studio is responsible for the end\-to\-end lifecycle of enterprise AI products and solutions, including discovery, experience and solution design, development, DevOps, deployment, adoption, and value realization.
The portfolio includes full\-stack digital solutions and agentic AI solutions leveraging technologies such as UiPath, large language models, machine learning, Databricks, AWS services, GitLab, Custom GPTs, Claude, and other emerging AI and automation technologies.
The Digital AI Product Analyst will support the analysis, design, development, delivery, and ongoing improvement of AI Studio products and solutions. This role will help connect business needs, user experience, product strategy, and technical delivery by partnering with business stakeholders, Product Managers, designers, architects, engineers, data scientists, platform owners, and external vendors.
The role will translate business needs and user challenges into clearly defined product requirements, user stories, process documentation, data requirements, acceptance criteria, implementation plans, and measurable success outcomes. The Specialist will contribute across the product lifecycle—from initial discovery and solution definition through development, testing, deployment, adoption, operational support, and value realization.
The Digital AI Product Analyst will lead requirements\-gathering and user\-story development sessions, support product backlog management, analyze data and business processes, coordinate user acceptance testing, and assist with product releases and production deployment. The role will help ensure product decisions are aligned with business priorities, user needs, technical feasibility, scalable architecture, measurable value, and applicable quality, security, privacy, and compliance requirements.
The successful candidate will apply strong business analysis, product analysis, data analysis, and stakeholder\-management capabilities to help deliver scalable digital and AI\-enabled solutions. The role will support the development of full\-stack applications, agentic AI solutions, GenAI applications, machine\-learning capabilities, data integrations, enterprise data products, intelligent automation, and digital workflows that improve business processes and user experiences.
Roles \& Responsibilities:
- Lead product discovery, requirements\-elicitation, process\-analysis, and user\-story development sessions with business stakeholders, Product Managers, subject matter experts, designers, architects, engineers, data teams, platform owners, and delivery partners.
- Translate business needs and user challenges into clear, development\-ready artifacts, including opportunity statements, product requirements, epics, features, user stories, acceptance criteria, process flows, functional requirements, and data requirements.
- Facilitate backlog\-refinement and requirements\-review sessions to ensure work is clear, complete, testable, technically feasible, and aligned with product strategy and business priorities.
- Maintain and support prioritization of the product backlog based on business value, strategic alignment, user impact, risk, dependencies, delivery readiness, and technical considerations.
- Partner with Product Managers, Delivery Leads, and technical teams to support roadmap planning, sprint planning, release planning, dependency management, and delivery execution.
- Analyze current\-state and future\-state processes to identify inefficiencies, manual activities, process and data gaps, business rules, risks, and opportunities for AI, automation, digital workflow, or process improvement.
- Support product and solution discovery by documenting business problems, stakeholder needs, user personas, pain points, desired future states, assumptions, risks, dependencies, success measures, and value drivers.
- Support experience and solution design by translating user and business needs into functional workflows, product capabilities, process designs, data interactions, and solution requirements.
- Perform data analysis and execute SQL queries across large datasets to validate requirements, define business logic and metrics, assess data quality, investigate issues, support testing, and inform product decisions.
- Partner with subject matter experts and data owners to define and align metric definitions, data sources, calculations, data usage, and data\-quality expectations.
- Collaborate with architects, developers, AI and machine\-learning engineers, data scientists, data engineers, automation developers, UX designers, and platform teams throughout the development lifecycle.
- Support the development and delivery of full\-stack applications, GenAI applications, agentic AI solutions, machine\-learning capabilities, intelligent automation, digital workflows, data integrations, and enterprise data products.
- Support solutions leveraging technologies such as UiPath, large language models, Databricks, AWS services, GitLab, Custom GPTs, Claude, APIs, web\-development frameworks, and related AI, automation, cloud, and data platforms.
- Engage with vendors and platform owners to evaluate functionality, technical constraints, integration options, licensing considerations, and opportunities to leverage existing enterprise technologies.
- Coordinate user acceptance testing, including test scenarios, test cases, expected results, test data, requirements traceability, defect documentation, retesting, and business approval.
- Support defect management, production deployment, and DevOps activities, including triage, tracking, business and environment readiness, release coordination, implementation planning, validation, and stakeholder communications.
- Support product operations by monitoring product usage, system performance, incidents, enhancement requests, user feedback, data\-quality concerns, and operational support needs.
- Drive adoption and value realization through user guidance, training, demonstrations, communications, adoption measures, business outcomes, value metrics, and post\-deployment analysis.
- Create leadership\-ready summaries and presentations covering business needs, product recommendations, scope, benefits, risks, dependencies, delivery progress, adoption, and realized value.
- Use approved AI and GenAI tools to improve analysis and documentation while ensuring product activities comply with applicable Amgen quality, security, privacy, responsible AI, data\-governance, and regulatory requirements.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The professional we seek is an individual with these qualifications.
Basic Qualifications:
Doctorate degree
OR
Master’s degree and 2 years of Computer Science, IT or related field
OR
Bachelor’s degree and 4 years of Computer Science, IT or related field
OR
Associate’s degree and 8 years of Computer Science, IT or related field
OR
High school diploma / GED and 10 years of Computer Science, IT or related field
Preferred Qualifications:
Functional Skills
Must\-Have Skills:
- Experience eliciting, analyzing, documenting, validating, and translating business requirements for software, data, digital, AI, automation, or technology implementation initiatives.
- Experience translating stakeholder needs into epics, features, user stories, acceptance criteria, process flows, functional requirements, data requirements, and development\-ready documentation.
- Experience applying Agile methodologies and supporting backlog refinement, sprint planning, user\-story development, defect management, release planning, and epic tracking.
- Experience using Jira, GitLab, or similar product\-development platforms to manage requirements, user stories, defects, dependencies, releases, and delivery activities.
- Experience performing data analysis to define, validate, and document business logic, key metrics, data requirements, testing outcomes, and product\-performance measures.
- Ability to write and execute SQL queries against large datasets to validate requirements, investigate issues, assess data quality, and support testing.
- Experience coordinating user acceptance testing, including test scenarios, test cases, expected results, test data, defect documentation, retesting, and business approval.
- Experience supporting production deployment, business readiness, release coordination, stakeholder communication, operational transition, and release management.
- Strong understanding of the product and software\-development lifecycle, including design, requirements, development, testing, deployment, adoption, operations, and value realization.
- Ability to analyze business processes and identify opportunities for AI, automation, digital workflow, data, system, or process improvement.
- Experience partnering across business, product, design, architecture, engineering, data, AI, platform, operations, and vendor teams to align requirements, priorities, testing, and delivery decisions.
- Strong communication, analytical thinking, structured problem\-solving, and attention to detail, with the ability to independently manage multiple priorities and resolve complex product, technical, process, or data issues in a matrixed environment.
Soft Skills and Behaviors
Must\-Have Behaviors:
- Applies structured, evidence\-based thinking by asking thoughtful questions, validating assumptions, analyzing available information, and using facts to guide product recommendations and delivery decisions.
- Demonstrates integrity and sound judgment when handling business information, product requirements, stakeholder input, value assumptions, data, and sensitive technical details.
- Focuses on delivering measurable value for patients, staff, business stakeholders, and product users.
- Builds trust through active listening, transparency, respectful challenge, collaboration, and reliable follow\-through.
- Communicates clearly and collaborates effectively across business, product, design, architecture, engineering, data, vendor, leadership, and global teams.
- Simplifies complex or ambiguous business and technical needs into clear requirements, user stories, process flows, data logic, and actionable recommendations.
- Takes accountability for the quality, completeness, traceability, and accuracy of product\-analysis deliverables.
- Operates with urgency while maintaining appropriate quality, compliance, ethics, privacy, security, scalability, operational readiness, and risk awareness.
- Demonstrates curiosity, continuous learning, and adaptability as technologies, product priorities, stakeholder expectations, user needs, and delivery constraints evolve.
Professional Certifications
- Business Analysis certification, such as CBAP, CCBA, ECBA, or Agile Business Analysis
- SAFe, Scrum Product Owner, or Scrum Master certification
- Product Management certification
- AWS, Databricks, UiPath, or related technology certification
- AI, GenAI, machine learning, automation, or digital\-transformation certification
- Data Analytics or Data Management certification
- DevOps, GitLab, or Lean Six Sigma certification
Success Measures:
- Clear, complete, testable, and development\-ready requirements, user stories, acceptance criteria, process flows, data definitions, and product documentation.
- Product backlogs and delivery priorities aligned with business value, strategic objectives, user needs, risk, dependencies, and technical feasibility.
- Strong cross\-functional alignment across business, product, design, architecture, data, engineering, testing, DevOps, operations, and external partners.
- Effective testing, release readiness, deployment, and operational transition, resulting in fewer requirement gaps, defects, delays, and instances of rework.
- Increased product adoption through effective stakeholder engagement, training, communications, user guidance, usage monitoring, and post\-release support.
- Measurable value realization demonstrated through improved productivity, process performance, user experience, data quality, cycle time, risk reduction, adoption, and other defined business 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 and well\-being. From our competitive benefits to our collaborative culture, we’ll support your journey every step of the way.
The expected annual salary range for this role in the U.S. (excluding Puerto Rico) is posted. Actual salary will vary based on several factors including but not limited to, relevant skills, experience, and qualifications.
In addition to the base salary, Amgen offers a Total Rewards Plan, based on eligibility, comprising of health and welfare plans for staff and eligible dependents, financial plans with opportunities to save towards retirement or other goals, work/life balance, and career development opportunities that may include:
- 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. Refer to the Work Location Type in the job posting to see if this applies
and make a lasting impact with the Amgen team.
careers.amgen.com
In any materials you submit, you may redact or remove age\-identifying information such as age, date of birth, or dates of school attendance or graduation. You will not be penalized for redacting or removing this information.
Application deadline
Amgen does not have an application deadline for this position; we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
Sponsorship
Sponsorship for this role is not guaranteed.
As an organization dedicated to improving the quality of life for people around the world, Amgen fosters an inclusive environment of diverse, ethical, committed and highly accomplished people who respect each other and live the Amgen values to continue advancing science to serve patients. Together, we compete in the fight against serious disease.
Amgen is an Equal Opportunity employer and will consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability status, or any other basis protected by applicable law.
We will ensure that individuals with disabilities are provided 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. 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.
Salary Context
This $110K-$148K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Amgen, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($129K) sits 40% below the category median. Disclosed range: $110K to $148K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Amgen AI Hiring
Amgen has 14 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Remote, US, Lisbon, ME, US. Compensation range: $139K - $244K.
Remote Work Context
Remote AI roles pay a median of $180,000 across 1,196 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.