Interested in this AI/ML Engineer role at Amgen?
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US - California - Los Angeles JOB ID: R-256713 LOCATION: US - California - Los Angeles WORK LOCATION TYPE: Remote DATE POSTED: Sep. 30, 2026 CATEGORY: Marketing SALARY RANGE: 149,349.25USD -202,060.75 USD What you will do
Let's do this. Let's change the world. In this vital role, reporting to the Director, Marketing AI Capabilities, you will serve as the day-to-day business product and transformation lead for the Marketing Command Center and related priority AI marketing capabilities. You will help execute Amgen's Future of Marketing strategy by translating marketer and customer needs, enterprise capabilities, relevant external trends, and innovation-pilot learnings into practical, governed workflows, product releases, adoption plans, and measurable outcomes.
Responsibilities include:
- Lead the day-to-day business product and delivery work for the Marketing Command Center, translating the strategy into a prioritized roadmap, user journeys, requirements, product backlog, release plans, pilot measures, and adoption activities.
- Work with analysts, marketers, brand teams, and commercial leaders to design role-aware Command Center and other tech-driven experiences that give each user the appropriate information, context, and action options. Ensure users can move from trusted insight to decision, approval, action, and learning using shared data definitions, evidence, and governance standards.
- Translate Future of Marketing priorities into practical operating-model requirements, including workflows, decision rights, human-review expectations, approval pathways, accountable ownership, feedback loops, and measures of marketer and customer value.
- Partner with Neural Nexus, enterprise data and analytics teams, Digital Technology and Innovation, and other capability owners to incorporate governed data products, analytics, knowledge assets, and AI-derived signals into marketing workflows.
- Support the evaluation and activation of AI partnerships and innovation pilots by coordinating business requirements, user testing, cross-functional readiness, governance needs, implementation dependencies, and recommendations to scale, optimize, or stop a capability.
- Drive cross-functional delivery, change readiness, and adoption across Marketing, Commercial, Medical, Legal/Regulatory, Privacy, Information Security, Market Access, Patient Services, Technology, agencies, and vendors. Develop practical playbooks, working sessions, and feedback mechanisms, and synthesize results, risks, dependencies, and recommendations for senior stakeholders.
What we expect of you
We are all different, yet we all use our unique contributions to serve patients. The strategic and collaborative professional we seek is a business product and transformation leader with these qualifications.
Basic Qualifications:
- Doctorate degree OR Master's degree and 3 years of experience in marketing, commercial strategy, digital product management, data and analytics, or a related field OR Bachelor's degree and 5 years of such experience OR Associate's degree and 10 years of such experience OR High school diploma / GED and 12 years of such experience.
- Demonstrated experience leading complex cross-functional initiatives from problem definition through implementation, adoption, and performance measurement.
- Experience translating customer or business needs into product requirements, roadmaps, implementation plans, or technology-enabled workflows.
- Working fluency in AI, data, and digital-product concepts sufficient to guide requirements, assess feasibility and risk, and partner credibly with technical experts.
- Ability to work effectively across marketing or commercial teams, data and analytics, technology, and relevant governance functions.
- Strong communication, facilitation, prioritization, and stakeholder-management skills, with the ability to create clarity across interdependent workstreams.
Preferred Qualifications:
- Experience in pharmaceutical, biotechnology, healthcare, or another highly regulated industry, particularly in commercial marketing, HCP engagement, patient services, market access, or omnichannel customer experience.
- Experience launching or scaling AI, generative AI, machine learning, predictive analytics, next-best-action, personalization, content intelligence, or decision-support capabilities.
- Experience with product delivery practices such as user research, journey mapping, Agile delivery, product backlogs, pilot design, service design, and adoption measurement.
- Familiarity with responsible-AI considerations, including data provenance, model limitations, privacy, security, human review, monitoring, and auditability.
- Experience working with Medical, Legal, Regulatory, Privacy, Information Security, Quality, or other formal governance partners in a regulated environment.
- Experience managing external vendors or partners and driving adoption across distributed teams.
What you can expect of us
As we work to develop treatments that take care of others, so we work to care for our teammates' professional and personal growth and well-being.
Clear and disciplined strategic vision for the future that leverages superior-quality products, operational excellence and top-shelf-talent.
Diverse and inclusive community of belonging, where teammates are empowered to bring ideas to the table and act.
In addition to the base salary, Amgen offers a Total Rewards Plan comprising 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 including:
- 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, including remote work arrangements, where possible
for a career that defies imagination
Objects in your future are closer than they appear. Join us.
careers.amgen.com
Application deadline
Amgen's application deadline for this position is on 10/22 and we will continue accepting applications until we receive a sufficient number or select a candidate for the position.
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.
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 $149K-$202K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 in Demand for This Role
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 $212,000 based on 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($175K) sits 17% below the category median. Disclosed range: $149K to $202K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Amgen AI Hiring
Amgen has 8 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist. Positions span Los Angeles, CA, US, Thousand Oaks, CA, US, Remote, US. Compensation range: $171K - $253K.
Location Context
AI roles in Los Angeles pay a median of $210,000 across 1,340 tracked positions.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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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