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Company Description
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 – immunology, oncology, neuroscience, and eye care – and products and services in our Allergan Aesthetics portfolio. For more information about AbbVie, please visit us at www.abbvie.com. Follow @abbvie on X, Facebook, Instagram, YouTube, LinkedIn and Tik Tok.
Job Description
As part of AbbVie’s Business Technology Solutions (BTS), Research and Development aligned technology team, R\&D Global Therapeutics is responsible for building and deploying scalable Artificial Intelligence (AI) Applications and Platforms.
The AI Product Owner will have ownership of some of these applications. In this role, you will be responsible for managing the development and deployment of AI products and solutions. You will work closely with data scientists, engineers, and business stakeholders to drive the product vision, strategy, and execution to deliver innovative AI solutions that meet market demands. You will identify, select, and consult on the appropriate AI technologies to be deployed. You will be responsible for defining and prioritizing the product backlog, ensuring the development team delivers high\-quality features that meet user needs and business goals.
As part of product ownership, this role is responsible for the translation of business needs into functional technology. This will include analyzing business processes; elicit, analyze and document business requirements; identify alternative solutions, assess feasibility and makes recommendations typically seeking to exploit and leverage new or existing technology components. This can also include performing data and process modeling; managing change; and leads, coordinates/performs testing, verification, and validation of requirements.
This role is roughly half consultative, and half technical. We are seeking an individual with proven experience and depth in Artificial Intelligence technologies, tools, and how they are applied to use cases.
In this role you will be responsible for:
- Product Strategy \& Roadmap: Define and communicate the product vision, strategy, and roadmap for AI platforms. Prioritize features, enhancements, and technical debt based on business value and technical feasibility.
- Product Development: Work closely with the development team to ensure understanding of user stories and acceptance criteria. Participate in sprint planning, daily stand\-ups, sprint reviews, and retrospectives. Make decisions on product functionality, balancing business value, technical feasibility, and user experience.
- Backlog Management: Define and prioritize the product backlog, ensuring it is aligned with business objectives and customer needs. Write clear, concise user stories with acceptance criteria that guide the development team. Continuously refine and update the backlog based on feedback and changing requirements
- Technical Expertise: Stay updated on the latest AI and machine learning technologies, trends, and best practices. Evaluate and select appropriate tools, frameworks, and platforms for AI development. Ensure the scalability, reliability, and performance of AI solutions.
- Work directly with business unit clients to understand specific business processes and business drivers and business strategy across multiple business units; identifies and communicates resulting needs and opportunities for business process improvement that can be enabled via technology.
- Investigates and understands capabilities of existing systems and technologies already in use across the business area and similar and interconnected business areas in AbbVie and investigates available technologies applied to this business area in industry; identifies information required to support the business strategy and leads the development of appropriate information management strategies, developing them as an integrated part of the business strategy.
- Develops, leads, or reviews the creation of information systems strategy to support the strategic requirements of multiple business areas. Identifies the business benefits of alternative strategies. Ensures compliance between business strategies and technology directions. May prepare testing plans to confirm that requirements and system design are accurate and complete. Conducts training.
- Stakeholder Management: Develops business relationships and integrates activities with other BTOs to ensure successful implementation and support of project efforts. Manages relationships between clients involved and BTOs to assure effective communication between the groups is occurring. Brokers services within BTS on behalf of customers; coordinates portfolio of solutions, and identifies interdependencies.
- Allies with other BTOs to remain current on project status, and inform customer management of progress; conversely, keeps BTS managers aware of user issues and resolves conflicts. Identifies the impact of any relevant statutory, internal or external regulations on the organization's use of information.
- Has defined authority and responsibility for a significant area of work, including technical, financial and quality aspects. Establishes organizational objectives and delegates assignments. Accountable for actions and decisions taken by self and subordinates.
- Accountable for the accuracy of the fit of the proposed business process improvements and the technical solution to the business needs and the information upon which the business justification and prioritization decisions are made. Also accountable for communicating the business need and drivers to development groups to assure the implementation phase can fulfill the business need.
Qualifications
Tools and skills you will use in this role:
- Technical Writing Skills
- Agile Scrum
- Jira
- Confluence
- Microsoft M365 Suite
- Strong Presentation Skills
Experiences that make you a strong fit for this role:
Required:
- Bachelor’s Degree with 7 years’ experience; Master’s Degree with 6 years’ experience; PhD with 2 years’ experience in Computer Science, Engineering, Data Science, or a related field.
- 3\+ years of experience as a Business Analyst, or similar role in a technical environment or product management.
- Proven track record of managing the development and launch of successful software products.
- Understanding of Generative AI, and traditional AI technologies, and how to apply them and select for best fit. Including hands\-on experience in AI and machine learning technologies.
- Knowledge of software development best practices
- Strong technical background with the ability to understand and discuss technical concepts
- Proficiency in Agile methodologies and tools (e.g., JIRA, Confluence).
- Excellent organizational and time management skills, with the ability to prioritize tasks effectively
- Strong leadership and team management abilities, with experience leading cross\-functional teams
- Excellent communication and interpersonal skills
Beneficial:
- Experience working in the Life Sciences industry and/or GXP validated systems
- Technical writing experience is a plus
- Experience working with a consulting background
- Technical project 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 short\-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:
https://www.abbvie.com/join\-us/reasonable\-accommodations.html
Salary Context
This $109K-$208K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($159K) sits 27% below the category median. Disclosed range: $109K to $208K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
AbbVie AI Hiring
AbbVie has 7 open AI roles right now. They're hiring across Research Scientist, AI/ML Engineer, AI Software Engineer. Positions span North Chicago, IL, US, Florham Park, NJ, US. Compensation range: $125K - $393K.
Location Context
AI roles in Chicago pay a median of $205,100 across 97 tracked positions. That's 6% below the national 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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