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About This Role
Job Details
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Req ID:
R040006
Location:
Philadelphia, PA \- \[Remote/Home\-Based]
Category:
Information Technology
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.
Brief Description:
The Associate Director, AI Development Lead is a strategic technical leader responsible for advancing Jazz Pharmaceuticals' enterprise AI capability and driving measurable business value through artificial intelligence and machine learning. Reporting to the Director, Enterprise AI, this role will spend much of its time in delivery — designing systems, writing production\-quality code, reviewing colleagues’ work, and guiding a small team of AI developers working across traditional data science (AI/ML) and modern Generative AI (GenAI) approaches.
AI development at Jazz follows a hub\-and\-spoke operating model. Business functions (spokes) own use\-case prioritization, stakeholder alignment, early\-stage ideation, and some proof\-of\-concept development. The central Digital Enterprise Capabilities (DEC) team (hub) serves as an advisory partner at any stage and progressively takes on a hands\-on development role as solutions mature — ensuring production\-grade standards for data readiness, model quality, code maturity, and reliability. The business is accountable for deciding what gets built; DEC owns how it gets built and deployed.
The role will also partner with the Director, Enterprise AI and the VP of Data, AI, and Research to help shape Jazz's enterprise AI strategy — operating model, governance, technology stack, best practices, and policies — and serve as a trusted subject\-matter expert to teams across Jazz who are evaluating use cases, prioritizing initiatives, and vetting vendors. As the team and capability mature, the balance of the role will naturally evolve toward broader technical oversight and a larger share of strategic work, while continuing to direct the most important technical decisions.
Essential Functions/Responsibilities
- Build and ship end\-to\-end ML solutions — data ingestion, feature engineering, model training, evaluation, deployment, and monitoring — across supervised, unsupervised, and other classical/statistical learning paradigms.
- Design and ship end\-to\-end GenAI solutions — including retrieval\-augmented generation (RAG) systems, agentic workflows, vector stores, and integrations with foundation model APIs.
- Across Gen AI and AI/ML, write production\-quality code (Python or related languages/frameworks as needed), with attention to readability, modularity, scalability, performance, and maintainability.
- Design and review architectures across Gen AI and AI/ML solutions that are secure, reusable, and rigorous. Work with the Enterprise AI Architect to ensure your designs follow our architectural standards.
- Debug, profile, and optimize solutions — including cost, latency, accuracy, and reliability trade\-offs.
- Support deployment and post\-deployment operations, including CI/CD, observability, model monitoring, drift detection, and incident response.
- Establish and continuously refine technical standards for development: reference architectures, design patterns, coding conventions, evaluation methodologies, and reusable components.
- Lead a small team of AI developers across traditional ML and Generative AI, setting clear technical direction, priorities, and quality expectations.
- Be accountable for the quality and outcomes of the team's work: solution design, code and model quality, documentation, and delivery.
- Mentor team members through code \& design review, both informal and formal feedback; build technical depth and ownership across the team.
- Plan and sequence delivery work in partnership with stakeholders, balancing scope, technical risk, and timelines.
- Help recruit, onboard, and grow AI talent as the team expands.
- Serve as a go\-to technical advisor to teams across Jazz, many of them non\-technical, helping them: understand how AI works, how to ensure human\-in\-the\-loop review and how to increase and optimize adoption of solutions while maintaining responsible and ethical use.
- Evaluate AI use cases and proposed solutions for technical feasibility, data readiness, value, and risk.
- Use expertise to support business in prioritizing AI initiatives based on business impact, technical feasibility, bandwidth constraints, solution complexity, and scalability. Within agreed upon product priorities, decide resource allocation and development activities for the team.
- Vet AI vendors and platforms in collaboration with our Enterprise AI Architect, as well as Infrastructure and Digital Security teams. Together, conduct technical due diligence on solution feasibility, data handling, security, transparency, and overall operational maturity. During such evaluations, this role will focus on solution fit for the use\-case, overall solution practicality, and vendor knowledge of AI’s capabilities and limitations.
- Support build\-vs\-buy decisions with a clear\-eyed view of time\-to\-value, total cost of ownership, extensibility, and vendor risk.
- Partner with the Director, Enterprise AI and the VP of Data, AI, and Research to help shape and execute Jazz's enterprise AI strategy, including operating model, governance, technology stack, best practices, and policies.
- Contribute to the maturation of the AI capability through reference architectures, MLOps practices, evaluation frameworks, and reusable components that make future delivery faster and more reliable.
- Help define and apply Responsible AI practices — explainability, evaluation, privacy, security, and risk management — across the work the team delivers and supports.
- Collaborate with Data Engineering, Enterprise Architecture, Infrastructure, InfoSec, and Compliance to ensure AI solutions meet enterprise standards.
- Build credibility quickly with senior technical and business stakeholders through effective communication and deep expertise.
Required Knowledge, Skills, and Abilities
- Demonstrated ability to lead, mentor, and be accountable for the technical work of others.
- Demonstrated experience leading offshore or near shore development teams required.
- Hands\-on technical depth in building and shipping ML/GenAI solutions to production.
- Proficiency in writing production\-quality code and designing AI/ML architecture.
- Strong understanding of core machine learning algorithms and statistical modeling techniques, model evaluation, and real\-world deployment considerations.
- Proficiency in one or more programming languages (e.g. Python, R) and ML frameworks (such as TensorFlow, PyTorch, scikit\-learn).
- Strong understanding of modern Generative AI concepts and approaches: LLMs, RAG, fine\-tuning, agentic patterns, evaluation, and guardrails.
- Significant hands\-on experience with at least one major cloud platform (AWS, GCP, or Azure); AWS or GCP preferred.
- Familiarity with AI governance and risk frameworks.
- Excellent communication skills with ability to explain complex technical concepts in clear, non\-technical terms, and a demonstrated ability to influence and guide stakeholders across various levels of an organization.
Required/Preferred Education and Licenses
- Bachelor’s degree or equivalent practical experience in a quantitative or technical field required. Advanced degree or equivalent preferred.
- 5–7 years of relevant experience in AI, machine learning, or data science required, 10\+ years preferred.
- Experience in pharmaceutical, life sciences, or another regulated industry strongly preferred.
- Experience evaluating and integrating third\-party AI/ML solutions and vendor platforms strongly 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 : 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 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 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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150. This role's midpoint ($192K) sits 12% below the category median. Disclosed range: $153K to $230K.
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.
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, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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