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About This Role
United States - Remote JOB ID: R-257062 LOCATION: United States - Remote WORK LOCATION TYPE: Remote DATE POSTED: Oct. 07, 2026 CATEGORY: Information Systems SALARY RANGE: 170,958.80USD -231,297.20 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. Amgen is advancing a broad and deep pipeline and portfolio of medicines to treat heart disease, obesity and obesity-related conditions, rare diseases, inflammatory conditions and cancer. 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 Digital Product Strategy and Innovation Manager (AI)
What you will do
Let’s do this. Let’s change the world. We are seeking a Senior Digital Product Manager, to join the AI & Data Lab Product Management Team. This role will lead the identification, evaluation, and validation of high-potential AI and data-driven opportunities across Amgen, shaping strategic opportunities and providing evidence-based recommendations that inform enterprise investment and scaling decisions.
The Senior Digital Product Manager will establish product discovery and validation approaches that enable Amgen to determine where AI can create meaningful business and patient value, where opportunities warrant further investment, and where initiatives should be redirected, paused, or stopped.
Operating at the intersection of business strategy, AI product innovation, data, technology, and enterprise transformation, this role will provide strategic product leadership across ambiguous, cross-functional opportunities and influence senior business and technology stakeholders on product direction, prioritization, investment, and scale.
Responsibilities:
- Lead the identification, framing, prioritization, and validation of a variety of AI-enabled business opportunities across Amgen, ensuring resources and validation efforts are focused on opportunities with the greatest potential enterprise value and strategic alignment.
- Evaluate whether proposed AI opportunities have the right data foundations, system integrations, architecture patterns, model capabilities, security controls, and operational support needed to move from concept to scalable solution.
- Collaborate with data scientists, ML engineers, data engineers, software engineers, solution architects, platform teams, and cybersecurity partners to shape solution concepts, understand technical tradeoffs, and identify dependencies early in the validation process.
- Design and execute rapid discovery sprints, prototypes, pilots, user research, and experiments to assess desirability, feasibility, viability, value, and risk. Use evidence to determine whether opportunities should advance, pivot, pause, or scale.
- Assess how LLMs, AI agents, machine learning models, NLP, automation, knowledge retrieval, analytics, and intelligent workflow tools can improve productivity, decision quality, scientific discovery, operational efficiency, and workforce effectiveness.
- Develop product requirements that account for user experience, data availability, model performance, output quality, explainability, reliability, latency, integration needs, governance, and ongoing measurement.
- Synthesize findings from validation work into clear product recommendations, opportunity briefs, technical feasibility assessments, business cases, and executive-ready narratives.
- Provide senior leaders with clear, evidence-based recommendations on investment, prioritization, scaling, and termination decisions, including explicit articulation of tradeoffs, uncertainties, dependencies, and risks.
- Own and actively manage a portfolio of AI opportunities across discovery, prototype, pilot, and scale-readiness stages, balancing strategic value, feasibility, risk, resource requirements, dependencies, and organizational readiness.
- Ensure validation efforts consider responsible AI, data privacy, model risk, cybersecurity, compliance, regulatory considerations, human oversight, change management, and enterprise scalability from the earliest stages of discovery.
- Lead executive and cross-functional decision forums that align business, scientific, technology, data, security, and governance stakeholders around opportunity priorities, product direction, technical tradeoffs, investment decisions, and paths to scale. Navigate competing perspectives and drive clarity and decisions in ambiguous or high-impact situations.
- Translate Amgen business and transformation priorities, emerging AI capabilities, technology strategy, and portfolio learnings into strategic product opportunity areas, identifying where reusable capabilities, platforms, data assets, or common patterns can create value across multiple functions rather than through isolated point solutions.
- Provide product leadership, coaching, and thought partnership to product managers and cross-functional teams, helping raise the maturity of AI product discovery, experimentation, evidence-based decision making, and product management practices across the organization.
- Act as a thought leader for AI product management, bringing external product, technology, and industry perspectives into Amgen and translating emerging AI capabilities into practical implications for enterprise product strategy.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The Senior Digital Product Manager professional we seek has these qualifications.
Basic Qualifications:
Doctorate degree and 2 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience
OR
Master’s degree and 6 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience
OR
Bachelor’s degree and 8 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience
OR
Associate’s degree and 10 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience
OR
High school diploma / GED and 12 years of Information Systems, Technology, Product, Digital, AI, Data, Business Transformation, or related experience
In addition to meeting at least one of the above requirements, you must have a minimum of 2 years of experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation of resources. Your managerial experience may run concurrently with the required technical experience referenced above.
Preferred Qualifications:
- 4+ years of product management, product strategy, innovation, consulting, digital transformation, or related experience.
- 2+ years of experience with AI, machine learning, data products, automation, LLMs, agents, NLP, analytics, or enterprise AI solutions.
- Demonstrated ability to operate effectively in high-ambiguity environments, independently frame complex problems, make sound recommendations with incomplete information, and drive alignment across stakeholders with competing priorities.
- Demonstrated experience leading AI or data products through discovery, validation, investment decision, and scale.
- Familiarity with generative AI, LLMs, retrieval-augmented generation, AI agents, prompt engineering, model orchestration, NLP, predictive analytics, or intelligent automation.
- Understanding of data architecture concepts, including data pipelines, data lakes, data warehouses, APIs, metadata, master data, data governance, and data quality.
- Experience shaping product strategy and investment decisions involving enterprise AI platforms and capabilities.
- Demonstrated ability to lead cross-functional product decisions across business, engineering, architecture, data, security, and governance teams.
- Experience establishing and leading product discovery and experimentation approaches across multiple complex initiatives.
- Ability to evaluate AI opportunities across desirability, feasibility, viability, value, risk, scalability, and data readiness.
- Experience developing business cases, opportunity assessments, technical feasibility summaries, executive narratives, or investment recommendations.
- Familiarity with biotech, pharmaceutical, R&D, manufacturing, medical, or enterprise business workflows.
- Demonstrated ability to influence senior executives and drive decisions through clear product narratives, recommendations, and evidence.
- Hands-on ability to analyze user feedback, usage data, workflow metrics, model outputs, experiment results, or AI product performance.
- Comfort working in fast-paced, cross-functional environments where priorities evolve quickly.
What you can expect of 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 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. We make all employment decisions without regard to race, color, religion, sex or any other characteristic protected by applicable law.
We provide reasonable accommodations to applicants and employees with disabilities, consistent with legal requirements. Please contact us to request accommodation.
Salary Context
This $170K-$231K range is above the median for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300. This role's midpoint ($201K) sits 5% below the category median. Disclosed range: $170K to $231K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Amgen AI Hiring
Amgen has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Positions span Remote, US, Los Angeles, CA, US. Compensation range: $140K - $269K.
Remote Work Context
Remote AI roles pay a median of $188,000 across 2,316 positions. About 6% 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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
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