Interested in this AI/ML Engineer role at Genmab?
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At Genmab, we are dedicated to building extra[not]ordinary® futures, together, by developing antibody products and groundbreaking, knock-your-socks-off KYSO antibody medicines® that change lives and the future of cancer treatment and serious diseases. We strive to create, champion and maintain a global workplace where individuals’ unique contributions are valued and drive innovative solutions to meet the needs of our patients, care partners, families and employees.
Our people are compassionate, candid, and purposeful, and our business is innovative and rooted in science. We believe that being proudly authentic and determined to be our best is essential to fulfilling our purpose. Yes, our work is incredibly serious and impactful, but we have big ambitions, bring a ton of care to pursuing them, and have a lot of fun while doing so.
Does this inspire you and feel like a fit? Then we would love to have you join us!
Vice President, Head of AI Center of Excellence (COE)
The Role
The Vice President, Head of AI Center of Excellence will lead Genmab’s enterprise AI strategy and its execution across Research, Development, Commercial, and enterprise functions.
The role will be accountable for converting advances in AI into measurable scientific, operational, and business value. This includes building AI solutions with Genmab teams, establishing responsible governance, managing strategic AI partnerships, and creating the capabilities required for Genmab to maximize the value of AI throughout the whole value chain.
The successful candidate will combine enterprise leadership, technical credibility, business judgment, and the ability to drive change across scientific, regulated, and corporate environments.
Work arrangement: This role offers flexibility to work away from the office for 20%–40% of a typical schedule. Employees may use this work schedule in increments of single days or multiple consecutive days, provided it does not exceed 40% within a 60-day period, and is approved by the hiring manager.
Key Responsibilities
- Define and execute Genmab’s enterprise AI strategy, roadmap, operating model, and investment priorities.
- Lead the AI Center of Excellence, including forward-deployed AI engineering, AI governance, and strategic AI partnerships.
- Work with functional leaders to identify and prioritize high-value AI opportunities across the company.
- Ensure AI initiatives move rapidly from experimentation to adoption, scaled deployment, and measurable impact.
- Establish a portfolio-management approach that balances near-term value, strategic capability building, and longer-term innovation.
- Build reusable AI platforms, products, engineering patterns, and governance pathways that reduce duplication and accelerate delivery.
- Ensure AI solutions meet appropriate standards for security, privacy, quality, reliability, validation, and responsible use.
- Lead strategic relationships with foundation-model providers, technology companies, and implementation partners.
- Advise the Executive Committee and other senior leaders on AI opportunities, investments, risks, and market developments.
- Develop AI talent, strengthen enterprise AI literacy, and create a culture of responsible experimentation and adoption.
- Define and track measures for adoption, business value, scientific impact, delivery speed, cost, risk, and capability maturity.
Qualifications
- A bachelor’s degree is required; An advanced scientific, and/or technical degree is highly preferred.
- 15+ years of leadership experience in artificial intelligence, data science, software engineering, digital transformation, or technology strategy.
- Demonstrated success building and scaling AI capabilities in a complex enterprise.
- Strong understanding of generative AI, foundation models, AI agents, machine learning, data platforms, and modern software development.
- Experience translating emerging technologies into deployed products and measurable business outcomes.
- Experience in pharmaceuticals, biotechnology, healthcare, or another highly regulated industry is strongly preferred.
- Ability to operate effectively across scientific, clinical, commercial, technology, quality, legal, and executive stakeholders.
- Strong people leadership, strategic judgment, communication, and change-management skills.
For US based candidates, the proposed salary band for this position is as follows:
$277,280.00-$415,920.00
The actual salary offer will carefully consider a wide range of factors, including your skills, qualifications, experience, and location. Also, certain positions are eligible for additional forms of compensation, such as discretionary bonuses and long-term incentives.
When you join Genmab, you’re joining a culture that supports your physical, financial, social, and emotional wellness. Within the first year, regular full-time U.S. employees are eligible for:
- 401(k) Plan: 100% match on the first 6% of contributions
- Health Benefits: Two medical plan options (including HDHP with HSA), dental, and vision insurance
- Voluntary Plans: Critical illness, accident, and hospital indemnity insurance
- Time Off: Paid vacation, sick leave, holidays, and 12 weeks of discretionary paid parental leave
- Support Resources: Access to child and adult backup care, family support programs, financial wellness tools, and emotional well-being support
- Additional Perks: Commuter benefits, tuition reimbursement, and a Lifestyle Spending Account for wellness and personal expenses
Further details on eligibility for compensation and benefits based on role will be provided during the recruitment process
About Genmab
Genmab is an international biotechnology company with a core purpose to improve the lives of patients through innovative and differentiated antibody therapeutics. For 25 years, its hard-working, innovative and collaborative team has invented next-generation antibody technology platforms and harnessed translational, quantitative and data sciences, resulting in a proprietary pipeline including bispecific T-cell engagers, antibody-drug conjugates, next-generation immune checkpoint modulators and effector function-enhanced antibodies. By 2030, Genmab’s vision is to transform the lives of people with cancer and other serious diseases with Knock-Your-Socks-Off (KYSO®) antibody medicines.
Established in 1999, Genmab is headquartered in Copenhagen, Denmark with international presence across North America, Europe and Asia Pacific. For more information, please visit Genmab.com and follow us on LinkedIn and X.
Genmab is committed to protecting your personal data and privacy. Please see our privacy policy for handling your data in connection with your application on our website Job Applicant Privacy Notice (genmab.com).
Please note that if you are applying for a position in the Netherlands, Genmab’s policy for all permanently budgeted hires in NL is initially to offer a fixed-term employment contract for a year, if the employee performs well and if the business conditions do not change, renewal for an indefinite term may be considered after the fixed-term employment contract.
Salary Context
This $277K-$415K range is above the 75th percentile 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 Genmab, 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. This role's midpoint ($346K) sits 63% above the category median. Disclosed range: $277K to $415K.
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.
Genmab AI Hiring
Genmab has 2 open AI roles right now. They're hiring across AI/ML Engineer. Based in Princeton, NJ, US. Compensation range: $292K - $415K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 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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