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About This Role
Company Description About AbbVie
AbbVie's mission is to discover and deliver innovative medicines and solutions that solve serious health issues today and address the medical challenges of tomorrow. We strive to have a remarkable impact on people's lives across several key therapeutic areas including immunology, oncology, and neuroscience - and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on LinkedIn, Facebook, Instagram, X and YouTube.
Job Description
As Head of Data and AI Technical Product Management, this leader serves as the global Commercial Data & AI technical product leader accountable for the operating model, portfolio strategy, investment trade-offs, product lifecycle, adoption, and product talent buildout for a large, regulated, global commercial data and AI ecosystem. The role builds and scales AbbVie’s Technical Data Product Ownership practice, serving as the IT counterpart to Commercial Business Product Owners and defining how technical data and AI products are discovered, prioritized, funded, built, launched, adopted, supported, measured, modernized, and retired. This leader translates Commercial product roadmaps and data consumer needs into durable, measurable technical product roadmaps while partnering with architecture, engineering, security, privacy, governance, analytics, and business teams. The role establishes the standards, lifecycle discipline, decision rights, adoption practices, funding mechanisms, and performance scorecards needed to move Global Commercial Data and AI from project-based delivery to product-based ownership at enterprise scale.
Responsibilities:
- Role dimensions: Serve as the global Commercial Data & AI technical product leader, owning the product operating model, multi-year portfolio strategy, investment trade-offs, lifecycle discipline, adoption strategy, and product talent buildout for a large, regulated, global commercial data and AI ecosystem. Lead the technical product roadmap across data products, AI-enabling capabilities, platform services, and self-service experiences aligned to Commercial priorities and enterprise risk expectations.
- Role scale: Own a multi-million-dollar annual Commercial Data & AI technical product portfolio supporting 30 commercial data domains, 30+ AI products and platform capabilities, 40+ brands/business units, and 120 countries. Build and scale a direct team of Technical Product Managers and product leaders, with intended responsibility for 10–20 product professionals and influence across 50–100+ matrixed engineering, AI, architecture, governance, analytics, and vendor resources as the organization matures.
- Ownership & autonomy: Own the Technical Data Product Management strategy, operating model, and product lifecycle for Commercial Data and AI capabilities, with accountability from discovery and roadmap integration through launch, adoption, support, versioning, modernization, and retirement. Partner with enterprise architecture, engineering, security, privacy, and governance leaders to make product trade-offs and ensure technical decisions align to business value, risk posture, and platform strategy.
- Decision-making: Accountable for product strategy, prioritization, roadmap trade-offs, adoption goals, lifecycle decisions, and value/risk recommendations for technical data and AI products. Define decision rights and working models with Commercial Business Product Owners and partners with architecture, engineering, security, privacy, and governance leaders on technical standards, risk acceptance, and compliance-by-design.
- Scope: Drive Global Commercial Data & AI technical product strategy across 40+ brands/business units, 120 countries, and hundreds of businesses, analytics, technology, and governance stakeholders. Partner with adjacent enterprise functions where Commercial data and AI products intersect, while maintaining clear primary accountability for the Commercial technical product ecosystem.
- Budget & investment: Own the annual engineering and technical portfolio budget and investment model; develop technical business cases, allocate funding across engineering initiatives, manage compute/cost-to-serve, and ensure vendor/partner spend aligns to product outcomes, architectural direction, and risk posture.
- Product talent buildout: Build and scale the Technical Data Product Management organization and talent bench, including hiring, coaching, performance management, succession planning, role standards, playbooks, communities of practice, and product leadership capability across direct and matrixed teams.
- Impact & influence: Serve hundreds of business, analytics, technology, and governance stakeholders globally; accountable for measurable technology outcomes such as reduced data latency, system uptime, engineering productivity gains, and compliance posture through improved data/AI reliability, discoverability, and adoption; operates at executive level, chairing or co-chairing technical steering committees and governance forums and influencing VP/SVP stakeholders through technical narratives, investment cases, operating model design, and performance metrics.
- Leadership expectations: Operate with enterprise mindset; make transparent value, risk, and capacity trade-offs; build inclusive and high-performing teams; influence senior stakeholders through clear narratives and metrics; raise product management standards across teams; and balance strategic ambition with pragmatic delivery and operational excellence.
Qualifications Required:
- Bachelor’s Degree plus 12+ years of progressive experience in technical product management, platform leadership, data and analytics, AI-enabled products, or a related discipline, including global portfolio ownership, operating model leadership, investment accountability, people leadership, and senior accountability for measurable outcomes in a complex enterprise environment.
- Demonstrated ability to define multi-year strategy, secure alignment with executive stakeholders, and translate business objectives into funded roadmaps and measurable value realization.
- Developed, launched, operated, and improved multiple production data products and AI-enabled capabilities at enterprise scale, with demonstrated discipline across discovery, lifecycle management, reliability, adoption, governance, and value realization.
- Experience leading a shift from project-based delivery to product-based operating models in a low-maturity or evolving organization, including role definition, intake, prioritization, roadmap standards, lifecycle management, product metrics, adoption practices, and change enablement.
- Proven product talent buildout and people leadership, including building or scaling teams of Product Managers and/or product leaders, hiring, coaching, performance management, succession planning, communities of practice, and product management standards.
- Demonstrated portfolio investment ownership, including annual OPEX/CAPEX planning, funding recommendations, capacity allocation, vendor/partner spend, cost-to-serve optimization, modernization trade-offs, and linkage of investment decisions to value, adoption, reliability, and risk outcomes.
- Deep understanding of modern data ecosystems and operating models: ETL/ELT, data warehouses/lakes, APIs/event streams, metadata/lineage, access controls, and governance at scale.
- Proven leadership partnering with engineering, architecture, security/privacy, and governance to drive quality, reliability (SLAs/SLOs), compliance-by-design, and platform modernization.
- Exceptional executive communication, influence, and prioritization skills; able to lead steering forums, resolve cross-domain trade-offs, and drive adoption and change across large organizations.
- Technology fluency expected: deep fluency across modern cloud, data, analytics, AI, metadata, lineage, catalog, governance, API, eventing, orchestration, and platform reliability patterns. Experience with specific tools such as AWS, Azure, GCP, Snowflake, Iceberg, Airflow, dbt, Alation, SageMaker, Microsoft AI capabilities, Anthropic, agentic platforms, SQL, Python concepts, APIs, and AI/BI tools is illustrative and helpful, but transferable technical product leadership across comparable ecosystems is most important.
Preferred:
- Experience in regulated industries, with life sciences/pharma Commercial data and analytics preferred, including sales, marketing, market access, omnichannel, CRM, and associated regulatory/privacy considerations.
- Experience building enterprise data/AI platforms and governance operating models (data catalog, data mesh/fabric concepts, stewardship, policy enforcement) across multiple business units and geographies.
- Experience managing complex vendor ecosystems and strategic partners (RFPs, contracting, SLAs, performance management), including multi-year platform programs and large-scale change management
Additional Information
Applicable only to applicants applying to a position in any location with pay disclosure requirements under state or local law:
- The compensation range described below is the range of possible base pay compensation that the Company believes in good faith it will pay for this role at the time of this posting based on the job grade for this position. Individual compensation paid within this range will depend on many factors including geographic location, and we may ultimately pay more or less than the posted range. This range may be modified in the future.
- We offer a comprehensive package of benefits including paid time off (vacation, holidays, sick), medical/dental/vision insurance and 401(k) to eligible employees.
- This job is eligible to participate in our long-term incentive programs.
Note: No amount of pay is considered to be wages or compensation until such amount is earned, vested, and determinable. The amount and availability of any bonus, commission, incentive, benefits, or any other form of compensation and benefits that are allocable to a particular employee remains in the Company's sole and absolute discretion unless and until paid and may be modified at the Company’s sole and absolute discretion, consistent with applicable law.
AbbVie is an equal opportunity employer and is committed to operating with integrity, driving innovation, transforming lives and serving our community. Equal Opportunity Employer/Veterans/Disabled.
US & Puerto Rico only - to learn more, visit https://www.abbvie.com/join-us/equal-employment-opportunity-employer.html
US & Puerto Rico applicants seeking a reasonable accommodation, click here to learn more:
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Salary Context
This $182K-$346K range is above the 75th percentile 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 AbbVie, 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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($264K) sits 25% above the category median. Disclosed range: $182K to $346K.
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.
AbbVie AI Hiring
AbbVie has 8 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Product Manager, Data Engineer. Positions span Irvine, CA, US, Mettawa, IL, US, North Chicago, IL, US. Compensation range: $236K - $346K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 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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