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About This Role
Location Gaithersburg, Maryland, United States Job ID R\-256801 Date posted 20/07/2026
Role purpose
AstraZeneca's bold ambition is to be a pioneer in science, lead in our disease areas and transform patient outcomes — and by 2030, to deliver 20 new medicines and industry‑leading growth. Biologics are central to that ambition, and Biopharmaceutical Development (BPD) is the R\&D function that turns biologic candidates into medicines. BPD develops the cell lines, bioprocesses, formulations, devices and analytical methods needed to advance biologic medicines through clinical development and approval where they can improve the lives of patients. As the portfolio grows in scale and complexity, BPD is increasingly adopting a Predict‑First CMC approach: FAIR data at source, greater use of modelling and digital twins, and AI\-enabled tools that help scientists find knowledge, make decisions and create regulatory content more efficiently.
The Senior Director, Machine Learning \& AI leads the ML \& AI team within BPD: a multidisciplinary group of specialists spanning data science, AI and data engineering, and applied machine learning research. The role is accountable for translating BPD's *Predict First* ambition into a coherent AI strategy and portfolio roadmap that transforms emerging technologies and promising ideas into trusted, scalable capabilities that deliver measurable scientific and business value. The Director defines the ML \& AI strategy for BPD, owns delivery of the AI portfolio within the digital transformation roadmap, and serves as BPD's senior technical interface with Enterprise AI and R\&D IT. The role is responsible for establishing a framework that rapidly tests and demonstrates value through proof\-of\-concepts (PoCs), accelerates adoption through iterative delivery, and enables the scaling of successful AI solutions across BPD.
In addition, the Director partners closely with Robotics \& Automation, Informatics, Digital Transformation, Enterprise AI, and R\&D IT teams to identify opportunities where ML \& AI can enhance scientific, operational, and business outcomes and to integrate AI capabilities into products, platforms, and workflows across BPD (e.g. Physical AI). The role provides strategic leadership on the data foundations required to enable AI at scale, including data architecture, governance, engineering, and platform capabilities, ensuring that high\-quality, accessible, and trusted data can support advanced analytics, machine learning, and AI solutions across the enterprise.
Success in this role requires a balance of strategic leadership and technical credibility. The Director will shape investment decisions, build organisational capability, drive adoption across BPD, influence senior stakeholders across BPD and the enterprise, and provide the technical judgement needed to guide delivery and manage risk.
Key accountabilities
Strategy and portfolio
- Define and maintain BPD’s multi\-year ML\&AI strategy, aligned with a Predict‑First CMC organisation, the BPD digital transformation roadmap and AZ’s AI30 ambitions.
- Be accountable for the BPD AI portfolio across the four pillars: AI Foundations \& Platforms, Knowledge Management, Modelling \& Digital Twins, and Submission \& Report Authoring.
- Set portfolio priorities across in\-flight, self\-funded and proposed initiatives, making clear, evidence\-based recommendations on when to build, buy, partner, pause or stop.
Technical leadership
- Provide senior technical oversight of model strategy, evaluation and deployment across predictive ML, mechanistic and hybrid models, protein sequence and structure models, knowledge graphs, RAG and agentic architectures.
- Set practical engineering standards for the team, including reproducibility, model risk management, MLOps, evaluation frameworks and human\-in\-the\-loop approaches for GxP\-adjacent use cases.
- Chair or lead technical review of the highest\-risk or highest\-value deliverables, ensuring decisions are well evidenced and risks are visible to the right governance forums.
Team leadership
- Lead and develop a high\-performing ML\&AI team of data scientists and AI/data engineers, growing capability and reach through permanent hires, secondments, PDRAs and vendor partnerships.
- Create the operating model, ownership and delivery discipline needed for a small specialist team to have enterprise\-level impact.
- Support AI training and culture change across BPD, helping scientists use AI well rather than simply use it more.
Cross‑functional delivery
- Work with modelling/AI, digitalisation and robotics transformation leads to align investment, dependencies and delivery plans across AI, data and automation.
- Partner with R\&D IT so enterprise platforms meet BPD’s scientific needs, and BPD requirements are visible in strategic platform roadmaps.
- Serve as BPD’s senior technical voice into Enterprise AI: adopt enterprise capability where it fits, escalate gaps, and shape shared offerings where BPD should not rebuild common capability
- Work closely with CMC Statistics, Informatics \& Software Engineering, and Robotics \& Automation Development colleagues so that ML\&AI outputs sit on sound statistical, software and laboratory foundations.Build Physical AI as an emerging BPD capability by partnering with Robotics \& Automation, Informatics, Digital Transformation, Enterprise AI and R\&D IT to connect ML\&AI models, agents and decision\-support tools with laboratory automation, instrumentation and closed\-loop experimental workflows.
Governance, compliance and risk
- Ensure BPD’s AI work aligns with AZ AI governance, data governance, information security and GxP expectations, as well as emerging external regulatory guidance on AI in CMC.
- Contribute to AZ's regulatory advocacy on AI in CMC where BPD's experience is directly relevant (e.g. via the CMC Strategy Board and PMF AI in CMC Working Group).
- Be accountable for responsible\-AI practice across the BPD portfolio, including model documentation, validation evidence, bias and robustness testing, and lifecycle management.
External innovation and partnerships
- Work with the AI Partnerships lead to bring useful external thinking into BPD through academic collaborations, consortia and vendor evaluations.
- Represent BPD externally through selected publications, conferences and standards forums where this supports the strategy.
Stakeholder engagement
- Brief digital transformation and BPD leadership on progress, value, trade\-offs and risk, distinguishing clearly between proven capability, active pilots and speculative opportunities.
- Act as a trusted advisor to BPD functional leaders on where AI can, and cannot, help them meet their objectives.
Qualifications and experience
Essential
- Advanced degree (MSc or PhD) in a quantitative discipline: computer science, machine learning, statistics, applied mathematics, physics, computational biology, chemical/biochemical engineering, or a closely related field. PhD plus 7 yr of relevant experience. MSc plus 10 ye of experience.
- Track record of leading ML\&AI teams that deliver production capability, not just prototypes, in regulated or scientifically demanding environments.
- Deep, current, hands\-on knowledge across the following: classical ML, deep learning, foundation or language models, agentic systems, digital twins, knowledge graphs, RAG and MLOps.
- Strong software engineering discipline; fluent in Python and modern ML tooling; comfortable working in cloud environments such as Azure or AWS, including containerised workloads and distributed compute.
- Experience turning ambiguous scientific or business problems into shaped AI solutions, including knowing when AI is not the right answer.
- Ability to influence senior stakeholders across scientific, technical and business functions, and to make clear recommendations under uncertainty.
- Experience building durable partnerships with IT/platform teams, external vendors and academic groups, with clear commercial, technical and delivery outcomes.
Desirable
- Domain understanding of biologics CMC, bioprocess development, formulation, analytical development, or regulatory submissions.
- Familiarity with FAIR data principles, data product thinking, ontologies and knowledge graphs applied to scientific data.
- Experience with GxP‑adjacent AI, model validation for regulated use, or contribution to regulatory advocacy on AI/ML.
- Peer\-reviewed publications or recognized external contributions in applied ML for life sciences.
What success looks like in the first 12–18 months
- Measurable time saved on knowledge retrieval across BPD, supported by an agent architecture and evaluation framework the team is confident to scale.
- At least one authoring pipeline moved from proof of concept into production use for a regulatory submission or comparability report.
- A working digital twin capability for a prioritised unit operation, with a defensible modelling strategy for the rest of the roadmap.
- An ML\&AI team that is known — inside BPD and beyond — for high‑quality delivery, clear technical judgement and honest communication about what AI can and cannot do.
- BPD requirements reflected in enterprise roadmaps, delivery commitments and platform investment decisions.
Date Posted
21\-Jul\-2026
Closing Date
30\-Jul\-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.
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 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 $218,750 based on 3,817 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,150.
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.
AstraZeneca AI Hiring
AstraZeneca has 3 open AI roles right now. They're hiring across AI/ML Engineer. Based in Gaithersburg, MD, US. Compensation range: $189K - $189K.
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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