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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
The Associate Director, Field Tools, Psych \& Migraine (CNS) is responsible for all aspects of the suite of tools provided to the field, including but not limited to planning, resourcing, strategy, implementation, socialization, and training. The ability to influence without authority across a wide array of stakeholders will be key to meeting the unique needs of each business in a timely manner. These stakeholders start with Sales Leadership, but also include Marketing, Commercial Operations, IT, Training, OEC, Legal, Med/Reg, and peers in the APEX group. to oversee the design, deployment, training, communications, and management of the Field Tools. Most importantly, the commitment and emotional intelligence to manage a team and focus on continuous development is paramount to success in this role.
Key Responsibilities:
Partnership: Must build and foster strong business relationships with sales leadership to ensure that the voice of the customer is always a key consideration in the development, support, maintenance, and enhancement of field tools. Must also choreograph cross\-functional stakeholders to ensure that the suite of Field Tools is consistent with brand strategy and delivering on the objectives of each respective tool.
System Management: Work closely with key Business, IT, OEC, Regulatory, and Legal stakeholders to ensure business requirements are well defined and, ultimately, approved. Demonstrates subject matter expertise regarding AbbVie business rules, processes, and all field tools. Proactively recommends improvements to streamline current processes and reduce cycle time. Leverages field network on co\-creation of new processes and solutions.
Project Management: Develop project plans (ongoing processes as well as new work streams) that integrate and align with cross\-departmental processes and dependencies. Collaborate with internal teams on data methodology, approach, framing, enhancements, field feedback, and new requests. Proactively identify efficiencies, mitigate risks, manage customer priorities, and deliver solutions to improve field force effectiveness.
Strategic Planning: Influence internal and external stakeholders to define long\-range strategic plans for field tools that employ an enterprise mindset while also driving field effectiveness. Infuse a customer focused mindset to deliver solutions and/or reduce current offerings thus ensuring field teams have everything they need to succeed and only what they need to succeed.
Communications and Training: Identify training/education and communications opportunities to pull through system adoption and usage. Effectively communicate the deployment of updates, enhancements, and launches. Participate in sales force calls, provide support during field meetings, construct and lead field tool task forces, and present systems capabilities to key internal stakeholders.
Customer Relationship Management: Create and maintain a wide and deep network of relationships across senior sales stakeholders to include VP's, Sales Directors, Analytics leadership, and the Field Planning \& Analytics Team. Viewed as a trusted partner and has established credibility with field sales management and functional support teams. Ability to influence key stakeholders to gain approval on strategic initiatives. Extensive experience in understanding the underlying business needs and translating into holistic field sales technology solutions.
Leadership and People Management: Will manage 1 direct report, with the goal of gaining another headcount in time. Coaching, mentoring, and providing timely feedback to highlight successes and address areas of opportunity are critical. Provide a clear set of goals and expectations that empower the team to address stakeholder needs with an enterprise mindset. Demonstrate forward thinking to ensure the team is adequately resourced for current and future needs.
Qualifications
- BA/BS degree required or equivalent experience, advanced degree preferred
- 5\+ years of pharmaceutical or relevant experience
- Motivate a team toward a shared purpose via leadership skills
- Cross\-functional collaboration
- Ability to influence without authority
- Excellent judgment and decision\-making skills
- Proven communication skills from peer interactions to direct report feedback to executive presentations
- Excellent written, verbal and presentation skills
- Proficiency with Microsoft PowerPoint and Excel
- Ability to clearly and confidently articulate complex and difficult concepts
- Confidence developing and delivering training and communications
- Excellent business acumen
- Understanding and experience working directly with pharmaceutical sales data sources
- Ability to thrive in a matrixed environment
- Willingness to innovate, adapt, and solve problems
- Results\-oriented
- Project management skills to meet and exceed agreed upon timelines
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 postingbased 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 orless 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 paidand 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 $141K-$268K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% 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 $166,983 based on 13,781 positions with disclosed compensation. Director-level AI roles across all categories have a median of $244,288. This role's midpoint ($205K) sits 23% above the category median. Disclosed range: $141K to $268K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
AbbVie AI Hiring
AbbVie has 50 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Mettawa, IL, US, Florham Park, NJ, US, St. Louis, MO, US. Compensation range: $97K - $490K.
Location Context
Across all AI roles, 7% (1,863 positions) offer remote work, while 24,200 require on-site attendance. Top AI hiring metros: Los Angeles (1,695 roles, $178,000 median); New York (1,670 roles, $200,000 median); San Francisco (1,059 roles, $244,000 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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