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US \- California \- Thousand Oaks
JOB ID: R\-247008 LOCATION: US \- California \- Thousand Oaks WORK LOCATION TYPE: Flex Commuter / Hybrid DATE POSTED: Jun. 12, 2026 CATEGORY: Sales \& Marketing Operations SALARY RANGE: \-
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.
Senior Manager, Agentic AI Business Solutions
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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 of Neural Nexus – Agentic AI Business Solutions, Senior Manager, you will be responsible for delivering prioritized Agentic AI solutions that support Amgen’s commercial and GCCI functions. This role focuses on the full execution lifecycle—design, development, implementation, rollout, and training—ensuring solutions are deployed on time, on scope, and with measurable business value.
You will partner closely with commercial insights, GCCI, AI\&D, and technical teams to convert business needs into operational GenAI capabilities. This is a hands\-on delivery role where attention to execution detail, tactical coordination, and outcome\-driven mindset will be critical for success.
This role is ideal for a technically strong delivery lead who thrives on driving solution deployment and enabling business teams to fully adopt GenAI capabilities.
Flexible Commuter role to Thousand Oaks, CA office. You will work on\-site 2\-3 days a week.
Strategic Leadership \& Roadmap Development
- Execute delivery plans for assigned Agentic AI use cases, ensuring alignment with Neural Nexus priorities and milestone timelines.
- Support business and platform teams by translating commercial requirements into clear delivery objectives and working plans.
- Contribute tactical input to broader program planning by identifying risks, blockers, and dependencies across assigned workstreams.
End\-to\-End Management of NN Platform Lifecycle
- Own the end\-to\-end execution of designated Agentic AI solutions, from intake through deployment, ensuring each phase—design, development, testing, and rollout—is completed to specification.
- Drive agile development cycles in collaboration with AI/ML and Engineering teams to ensure models and agents meet business needs and reliability standards.
- Coordinate internal readiness activities, including UAT, access setup, system integration, and documentation to support solution go\-live.
Cross\-Functional Team Orchestration \& Collaboration
- Partner with GCCI, Commercial Insights, and AI\&D to ensure that Agentic AI use cases are correctly scoped, validated, and configured for maximum impact.
- Serve as a key tactical liaison between technical delivery teams and business users, ensuring clear handoffs, documentation, and timelines are maintained.
- Work cross\-functionally to troubleshoot blockers and maintain delivery velocity across teams and workstreams.
Executive Stakeholder Management \& Strategic Communication
- Provide status updates and delivery dashboards for assigned Agentic AI initiatives, focused on progress, risks, and delivery health.
- Collaborate with platform leadership to align solution rollout timing and messaging with broader program milestones.
- Communicate implications of AI outputs, system performance, and solution scope changes to business stakeholders with clarity and transparency.
Driving Platform Adoption \& Value Realization
- Coordinate training sessions, office hours, and enablement resources to ensure business users can confidently utilize the deployed Agentic AI solutions.
- Partner with change enablement and analytics teams to track adoption metrics and gather user feedback post\-deployment.
- Identify usage trends and value realization metrics to help measure the impact of Agentic AI capabilities on operational efficiency or insight quality.
Change Management \& Continuous Improvement
- Support change management by ensuring that solution delivery includes all necessary comms, onboarding, and process readiness steps.
- Facilitate feedback loops to identify areas for enhancement or retraining based on user interaction and system outputs.
- Help maintain and iterate on delivery processes and documentation to ensure consistency, quality, and sustainability of deployed Agentic AI solutions.
What we expect of you
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We are all different, yet we all use our unique contributions to serve patients. The professional we seek is a collaborative leader with these qualifications.
Basic Qualifications:
Doctorate degree and 2 years of commercial analytics and/or operationalizing GenAI solutions experience
OR
Master’s degree and 4 years of commercial analytics and/or operationalizing GenAI solutions experience
OR
Bachelor’s degree and 6 years of commercial analytics and/or operationalizing GenAI solutions experience
OR
Associate’s degree and 10 years of commercial analytics and/or operationalizing GenAI solutions experience
OR
High school diploma / GED and 12 years of commercial analytics and/or operationalizing GenAI solutions experience
Preferred Qualifications:
- Bachelor’s degree and 6\+ years of experience in commercial analytics and/or operationalizing GenAI solutions.
- Strong track record of executing complex AI/ML delivery initiatives—especially GenAI agents or copilots that automate tasks or drive decision workflows.
- Strong Technical Aptitude (AI/ML): Solid conceptual and practical understanding of Generative AI, agent architectures, development lifecycle, and technical delivery principles.
- Ability to coordinate across technical and business teams, translate complex requirements, and keep delivery on track under shifting priorities.
- Hands\-on experience supporting commercial or field teams, ideally within a regulated enterprise setting.
- Proven ability to deploy, train, and support business teams on new digital tools or platforms.
- Comfortable operating in high\-ambiguity, fast\-paced, and cross\-functional delivery environments.
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.
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 3,823 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 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Amgen AI Hiring
Amgen has 4 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Thousand Oaks, CA, US, Remote, US. Compensation range: $200K - $390K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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