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About This Role
United States \- Remote
JOB ID: R\-247450 LOCATION: United States \- Remote WORK LOCATION TYPE: Remote DATE POSTED: Jun. 16, 2026 CATEGORY: Information Systems SALARY RANGE: 179,085\.65USD \-242,292\.35 USD
Join Amgen’s Mission of Serving Patients
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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.
Associate Director, Finance Technology, AI Data \& Innovation
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What you will do
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Let’s do this. Let’s change the world. In this vital role you will lead the development and execution of the CFO organization’s digital, AI, data, and innovation agenda. This role will partner closely with Finance senior leaders, Digital/Technology, Data, Cybersecurity, Compliance, and cross\-functional stakeholders to identify, prioritize, and deliver technology\-enabled solutions that improve Finance productivity, decision\-making, operational effectiveness, and enterprise value.
The role will serve as a strategic connector between Finance business needs and technology capabilities, helping translate priorities into scalable solutions, responsible AI use cases, data\-driven insights, and new ways of working. This leader will also play a key role in building Finance technology literacy, strengthening adoption, and ensuring innovation efforts are governed, measurable, and aligned to business priorities.
What we expect of you
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We are all different, yet we all use our unique contributions to serve patients. The Finance Technology AI Data \& Innovation professional we seek is a leader with these qualifications.
Responsibilities
- Lead the Finance technology, AI, data, and innovation roadmap aligned with CFO organization priorities.
- Partner with Finance and cross\-functional leaders to identify and deliver technology\-enabled business improvements.
- Translate Finance business needs into scalable technology, AI, automation, and analytics solutions.
- Manage the Finance technology and innovation portfolio, including prioritization, governance, risk management, and value realization.
- Lead the intake, prioritization, and execution of Finance AI, data, and automation initiatives.
- Advance Finance data and analytics capabilities through improved reporting, dashboards, self\-service analytics, and data governance.
- Drive process simplification, reporting optimization, automation, and data\-driven decision making across Finance.
- Partner with Digital/IT and enterprise teams to develop, scale, and operationalize technology solutions and prototypes.
- Ensure Finance technology initiatives comply with enterprise architecture, cybersecurity, privacy, compliance, and internal control standards.
- Establish governance frameworks for Finance technology, AI, and data initiatives, including approvals, controls, adoption, and performance tracking.
- Promote responsible AI adoption and strengthen technology, data literacy, and analytical capabilities across the CFO organization.
- Lead change management, stakeholder engagement, communications, and training to accelerate adoption of new technologies.
- Serve as the primary liaison between Finance, Digital/Technology, Data, Cybersecurity, and other enterprise functions.
- Provide executive portfolio reporting, strategic recommendations, and oversight to support prioritization, execution, and delivery across initiatives.
Basic Qualifications:
Doctorate degree and 3 years of Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation experience
OR
Master’s degree and 7 years of Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation experience
OR
Bachelor’s degree and 9 years of Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation experience
OR
Associate’s degree and 12 years of Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation experience
OR
High school diploma / GED and 14 years of Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation experience
In addition to meeting at least one of the above requirements, you must have a minimum of 3 years experience directly managing people and/or leadership experience leading teams, projects, programs, or directing the allocation or resources. Your managerial experience may run concurrently with the required technical experience referenced above
Preferred Qualifications:
- Bachelor’s degree in Finance, Business, Technology, Data Analytics, Information Systems, or related field.
- Relevant experience in Finance, digital transformation, technology enablement, data analytics, AI, innovation, or business process transformation.
- Experience partnering with senior stakeholders and cross\-functional teams in a matrixed environment.
- Demonstrated ability to translate business needs into technology, data, or process solutions.
- Strong project, program, or portfolio management skills.
- Experience in Finance transformation, FP\&A, corporate finance, digital finance, or enterprise technology initiatives.
- Experience with AI use cases, automation, data visualization, dashboarding, analytics, or self\-service technology tools.
- Familiarity with tools such as Power BI, Tableau, Alteryx, SAP, Anaplan, Microsoft Copilot, CustomGPTs, workflow tools, or similar platforms.
- Understanding of data governance, cybersecurity, privacy, compliance, and internal control considerations.
- Strong business acumen with ability to connect technology initiatives to Finance priorities and measurable business outcomes.
- Excellent communication, storytelling, stakeholder management, and influencing skills.
- Ability to operate in ambiguity, manage competing priorities, and move ideas from concept to execution.
What you can expect of us
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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.
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careers.amgen.com
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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
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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
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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.
Salary Context
This $179K-$242K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 2130 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 4,133 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $185,000 based on 13,200 positions with disclosed compensation. Director-level AI roles across all categories have a median of $250,000. This role's midpoint ($210K) sits 14% above the category median. Disclosed range: $179K to $242K.
Across all AI roles, the market median is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Safety ($274,200) and AI Engineering Manager ($268,700). By seniority level: Entry: $97,760; Mid: $165,778; Senior: $227,400; Director: $250,000; VP: $250,000.
Amgen AI Hiring
Amgen has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Remote, US, Thousand Oaks, CA, US. Compensation range: $200K - $390K.
Remote Work Context
Remote AI roles pay a median of $173,300 across 2,012 positions. About 14% 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 4,133 open positions tracked in our dataset. By seniority: 106 entry-level, 1,901 mid-level, 1,663 senior, and 463 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (583 positions). The remaining 3,532 roles require on-site or hybrid attendance.
The market median for AI roles is $200,700. Top-quartile compensation starts at $254,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Safety ($274,200 median, 57 roles); AI Engineering Manager ($268,700 median, 42 roles); Research Engineer ($260,000 median, 442 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 4,133 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,865), Data Scientist (339), AI Software Engineer (313). 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 (1,901) and senior (1,663) 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 463 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (583 positions), with 3,532 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 $200,700. Top-quartile roles start at $254,000, and the 90th percentile reaches $307,500. 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 $274,200 median, while Prompt Engineer roles sit at $140,000. 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 (2,128 postings), Aws (1,324 postings), Azure (1,003 postings), Rag (916 postings), Gcp (817 postings), Pytorch (655 postings), Prompt Engineering (639 postings), Claude (571 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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