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If you are a current Jazz employee please apply via the Internal Career site.
Jazz Pharmaceuticals is a global biopharma company whose purpose is to innovate to transform the lives of patients and their families. We are dedicated to developing life\-changing medicines for people with serious diseases — often with limited or no therapeutic options. We have a diverse portfolio of marketed medicines, including leading therapies for sleep disorders and epilepsy, and a growing portfolio of cancer treatments. Our patient\-focused and science\-driven approach powers pioneering research and development advancements across our robust pipeline of innovative therapeutics in oncology and neuroscience. Jazz is headquartered in Dublin, Ireland with research and development laboratories, manufacturing facilities and employees in multiple countries committed to serving patients worldwide. Please visit www.jazzpharmaceuticals.com for more information.
The Associate Director, Commercial AI Technical Lead provides senior‑level technical expertise and hands‑on leadership across AI and advanced analytics initiatives for the Commercial function of Jazz. This role acts as a key individual contributor and technical advisor, partnering closely with the Commercial organization and Digital Enterprise Capabilities Data \& AI colleagues to ensure AI solutions are technically sound, scalable, compliant, and value‑driven. The role is responsible for supporting build versus buy evaluations, assessing the quality, robustness, and long‑term viability of AI solutions, and contributing to the prioritization of commercial AI use cases based on business impact, feasibility, data readiness, and risk. Strong applied data science skills and pharmaceutical commercial experience are critical to success in this role.
Essential Functions/Responsibilities
- Apply data science expertise for AI and advanced analytics solutions across Commercial use cases such as forecasting, targeting, segmentation, personalization, omnichannel engagement, and commercial optimization.
- Provide technical expertise in Generative AI applications for field teams (e.g., pre\-call planning), marketing teams (e.g., content generation or market research), operations teams (e.g., knowledge summarization and search), etc.
- Evaluate the technical quality of AI solutions, including (where applicable) LLM strategy, solution architecture, model design, featurization, data requirements, validation approaches, explainability, monitoring, and scalability.
- Review and challenge analytical approaches, assumptions, and outputs from internal teams and external vendors to ensure solutions meet Jazz standards for rigor, trust, and compliance.
- Support technical assessments of build versus buy options for commercial AI capabilities, considering time to value, cost, scalability, extensibility, vendor dependency, and long term ownership.
- Participate in technical due diligence of vendor solutions, including review of methodologies, data usage, solution transparency, and operational readiness.
- Contribute clear technical input and recommendations to inform investment and solution implementation decisions. Evaluate build versus buy frameworks.
- Support prioritization of commercial AI initiatives by assessing feasibility, data readiness, implementation complexity, adoption readiness, and potential business impact.
- Partner with commercial and analytics stakeholders to define success metrics and ensure AI solutions are designed to deliver measurable value.
- Define pilot or MVP solutions that test solution applicability, confirm value measurement and define supporting process changes.
- Identify opportunities to reuse, standardize, or scale AI capabilities across brands, markets, or commercial functions.
- Remain hands on as needed, contributing to or reviewing development, experimentation design, feature engineering, and analytical pipelines.
- Apply best practices in data science, including model validation, bias assessment, explainability, and documentation appropriate for a regulated environment.
- Support the responsible application of advanced analytics, machine learning, and generative AI techniques in commercial contexts.
- Partner closely with Commercial, DEC Data Engineering, Architecture, and Security teams and Privacy and Compliance teams to support delivery of scalable, secure AI solutions.
- Translate complex technical concepts into clear, actionable guidance for non\-technical stakeholders.
- Ensure commercial AI solutions align with enterprise data, AI, security, and governance standards.
Required Knowledge, Skills, and Abilities
- Solid understanding of machine learning techniques, model evaluation, and real world deployment considerations.
- Experience evaluating third party AI solutions and comparing them with internally developed approaches.
- Experience working in regulated environments with awareness of privacy, compliance, and explainability requirements.
- Strong communication and collaboration skills with the ability to influence without formal authority.
- Experience supporting AI initiatives in a matrixed enterprise environment.
- Experience working with commercial data sources such as CRM, claims, promotional, and digital engagement data.
Required/Preferred Education and Licenses
- Bachelor’s degree with advanced training or experience in Data Science, Statistics, Computer Science, Engineering, Social science fields (quantitative psych, quantitative sociology, or economics) or a related quantitative field; advanced degree preferred.
\#LI\-Remote
*Jazz Pharmaceuticals is an equal opportunity/affirmative action employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, or any characteristic protected by law.*
FOR US BASED CANDIDATES ONLY
Jazz Pharmaceuticals, Inc. is committed to fair and equitable compensation practices and we strive to provide employees with total compensation packages that are market competitive. For this role, the full and complete base pay range is: $153,600\.00 \- $230,400\.00
Individual compensation paid within this range will depend on many factors, including qualifications, skills, relevant experience, job knowledge, and other pertinent factors. The goal is to ensure fair and competitive compensation aligned with the candidate's expertise and contributions, within the established pay framework and our Total Compensation philosophy. Internal equity considerations will also influence individual base pay decisions. This range will be reviewed on a regular basis.
At Jazz, your base pay is only one part of your total compensation package. The successful candidate may also be eligible for a discretionary annual cash bonus or incentive compensation (depending on the role), in accordance with the terms of the Company's Global Cash Bonus Plan or Incentive Compensation Plan, as well as discretionary equity grants in accordance with Jazz's Long Term Equity Incentive Plan.
The successful candidate will also be eligible to participate in various benefits offerings, including, but not limited to, medical, dental and vision insurance, 401k retirement savings plan, and flexible paid vacation. For more information on our Benefits offerings please click here: https://careers.jazzpharma.com/benefits.html.
Salary Context
This $153K-$230K range is above the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Jazz Pharmaceuticals, 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. Director-level AI roles across all categories have a median of $247,800. This role's midpoint ($192K) sits 6% above the category median. Disclosed range: $153K to $230K.
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.
Jazz Pharmaceuticals AI Hiring
Jazz Pharmaceuticals has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Philadelphia, PA, US. Compensation range: $230K - $230K.
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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