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About This Role
Location Gaithersburg, Maryland, United States Job ID R-261510 Date posted 12/10/2026
Make a greater impact on lives through a digitally enabled enterprise.
We are working towards our aspiration of shaping the future of Pharma. Backed by investment at every step, we harness the latest technology, data, and innovative ways of working to introduce new approaches that put Digital at AstraZeneca’s heart.
Be part of a chapter that’s still being written. Trusted and empowered to change the way things are done, with the tools and support to get you there. Here we are free to unleash our curiosity and turn thinking into reality where smart risks and quick decisions come together to power disruption and innovation.
This is a product leadership role at the center of how AstraZeneca’s Medical Affairs organizations work. You will own the end-to-end product development of AI-powered solutions that put AI, agentic workflows, and evaluation and quality framework into the hands of medical colleagues across headquarters and the field, changing how scientific evidence is generated, communicated, and acted on for patients and healthcare professionals.
The Role
The Director, AI Product Development, Medical is part of the AI for Commercial (AIC) team within the Enterprise AI organization. You will lead the design, build, launch, and adoption of GenAI and ML-powered products that serve Medical Affairs organizations across Oncology (OBU), BioPharma (BBU), and Rare Disease (RDU), at Global and market levels. Your products will change how medical colleagues generate insights, engage scientifically with healthcare professionals, and translate evidence into decisions. You define the roadmap, drive cross-functional execution, and are accountable for measured business impact.
Key Responsibilities
- Own the full AI product lifecycle: from problem definition and customer research through AI development, UX design, deployment, adoption, and measurement. This means shipping products, not delivering reports.
- Lead GenAI and agentic AI product development for Medical Affairs. Define product requirements and functional specifications for AI-powered applications, agentic workflows, and evaluation and quality framework that serve headquarters and field medical teams across therapeutic areas.
- Translate high-value medical use cases into product requirements. Partner with Medical leadership, Field Medical Excellence, Medical Informatics, Scientific Communications & Content Strategy, Evidence Generation (RWE/HEOR), Scientific Engagement, and Patient Solutions to align product capabilities with therapy area strategy, scientific engagement priorities, and field workflows.
- Build on enterprise AI foundations rather than BU-specific silos. Leverage shared platforms, data assets, and reusable components so that capabilities built for one BU extend to others. Actively challenge duplicative or one-off solution requests and redirect them toward common capability.
- Partner with technical delivery leads, engineering, data science, and IT teams to translate requirements into scalable and governed solutions and ensure high-quality, on-time execution. Establish clear RACI, maintain alignment on priorities, and drive accountability across a cross-functional delivery model.
- Drive adoption and value realization. Define success criteria before launch; track usage, output quality, and downstream impact post-launch, including cycle time reduction for insight synthesis, content development, and planning. Communicate results to leadership with clarity.
- Operate within responsible AI and medical governance. Navigate scientific exchange standards, promotional and non-promotional boundaries, privacy and data access controls, vendor management, and IT approval processes. Ensure human-in-the-loop oversight and appropriate documentation for all deployed products.
- Set the product roadmap and prioritization across BUs. Balance competing therapy area demands, manage scope with transparency, and sequence delivery to protect critical milestones while building toward shared capability rather than one-off solutions.
- Stay current with GenAI, agentic AI, and pharma AI trends. Identify emerging capabilities with strategic fit, pilot solutions with speed and rigor, and champion a platform-centric approach that enables modular reuse, scalability, and accelerated deployment across BUs, functions, and markets.
Minimum Requirements
- Advanced degree (MS, PhD, PharmD, or MD) in a quantitative or scientific discipline (Computer Science, Statistics, Engineering, Life Sciences, Epidemiology, or equivalent)
- 7+ years of experience building and shipping data science or AI products in a commercial or life sciences setting
- 3+ years in Pharmaceutical or Life Sciences with direct exposure to Medical Affairs; familiarity with medical data domains such as scientific insights, publications, medical information, real-world evidence, or HCP engagement
- Demonstrated experience owning an AI product end-to-end: requirements, development, deployment, and post-launch measurement
- Working knowledge of modern AI approaches (LLMs, retrieval-augmented generation, agentic workflows) sufficient to scope use cases, evaluate technical tradeoffs, and write requirements that engineering teams can execute against
- Strong product thinking: ability to define user needs, write clear requirements, prioritize a backlog, and communicate tradeoffs to stakeholders
- Experience delivering solutions on shared enterprise platforms in partnership with IT and technology functions
Desirable Requirements
- PhD, PharmD, or MD with a track record of translating scientific research into applied products
- Experience delivering AI products for Medical Affairs (insight analytics, content augmentation, scientific engagement, or predictive engagement)
- Working knowledge of medical platforms and integration patterns (Veeva, Salesforce, knowledge management, DAM)
- Understanding of global and local medical regulations, scientific exchange standards, and responsible AI governance practices
- Background spanning multiple functions: Medical Affairs, Commercial, Market Access, or Insights & Analytics
- Demonstrated ability to drive user adoption at scale: role-based training, change management, and usage analytics
- Experience managing external vendors and consultants against defined deliverables and timelines
- Strong executive communication skills; comfortable presenting product roadmaps and business cases to senior leadership in a matrixed organization
When we put unexpected teams in the same room, we unleash bold thinking with the power to encourage life-changing medicines. In-person working gives us the platform we need to connect, work at pace and challenge perceptions. That's why we work, on average, a minimum of three days per week from the office. But that doesn't mean we're not flexible. We balance the expectation of being in the office while respecting individual flexibility. Join us in our unique and ambitious world. #EAI
Are you ready to bring new insights and fresh thinking to the table? Fantastic! We have one seat available, and we hope it’s yours. Apply today.
The annual base pay (or hourly rate of compensation) for this position ranges from $186,014 to $279,021. Our positions offer eligibility for various incentives—an opportunity to receive short-term incentive bonuses, equity-based awards for salaried roles and commissions for sales roles. Benefits offered include qualified retirement programs, paid time off (i.e., vacation, holiday, and leaves), as well as health, dental, and vision coverage in accordance with the terms of the applicable plans.
Date Posted
13-Oct-2026 Closing Date
26-Oct-2026 Our mission is to build an inclusive environment where equal employment opportunities are available to all applicants and employees. In furtherance of that mission, we welcome and consider applications from all qualified candidates, regardless of their protected characteristics. If you have a disability or special need that requires accommodation, please complete the corresponding section in the application form.
Salary Context
This $186K-$279K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At AstraZeneca, 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 $211,950 based on 13,048 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($232K) sits 10% above the category median. Disclosed range: $186K to $279K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
AstraZeneca AI Hiring
AstraZeneca has 7 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Gaithersburg, MD, US, Shanghai, Shanghai, China, London, England, UK. Compensation range: $206K - $279K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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