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About This Role
Location Boston, Massachusetts, United States Job ID R\-253376 Date posted 28/05/2026
We're building a connected, end\-to\-end Enterprise AI engine \- uniting data foundations, AI technology, process reinvention, and business\-facing AI to accelerate results across the whole value chain. Success depends on being exceptional connectors: you'll actively leverage existing capabilities, celebrate and promote reuse, export breakthrough ideas across geographies and functions, and obsess over scaling impact rather than building in isolation. If you thrive in high\-collaboration environments where your role is to turn complex, cross\-functional problems into reusable, enterprise\-wide capabilities \- and where the measure of success is adoption and scale, not just innovation \- you'll have the platform (and sponsorship) to make it real.
About AISI
AI Science \& Innovation (AISI) sits at the centre of AstraZeneca's R\&D AI transformation. Our remit is to build, buy and deliver the AI models and agents that change pipeline outcomes, across discovery, translational science, biomarkers and clinical development.
Role Overview
AstraZeneca is building a world\-class AI capability for Clinical Development within AISI to accelerate the design, conduct, and analysis of clinical trials across our Oncology and BioPharmaceuticals pipeline. We are hiring the Head of AI for Clinical Operations to lead the science and the team that turns AI promise into real\-world operational improvements in how AstraZeneca runs its trials.
This is one of the highest impact challenges in AI for healthcare. Trials run for years, span thousands of clinicians and patients across dozens of countries, and generate streams of noisy, heterogeneous, regulated data. The playbook for AI in this environment will be developed in the next two to three years. You will advance the science of AI for trial execution and bring better treatments to patients, faster, while adhering to the highest evidentiary, GxP, and patient safety standards. We're hiring someone who sees this high bar as the reason to come, not a reason to hesitate.
The AI for Clinical Development function is being built from the ground up, and you'll help define how AstraZeneca does AI for clinical operations. Expect an outsized voice with regulators, scientific consortia, and external partners during the narrow window when the rules of the road for agentic AI, AI/ML governance, and AI in trial conduct are being written.
We hire for learning agility and technical excellence. The strongest candidate is the person who learns fast, is comfortable with ambiguity, prototypes early, fails forward, and partners credibly across communities (ML, clinical, biostatistics, regulatory). We’re seeking an engineer\-leader with the leadership, technical depth, and curiosity to develop, adapt, and apply the most advanced methods in AI (e.g., agentic systems, multimodal foundation models, tool\-use and orchestration, domain\-specific evaluation environments) to clinical operations workflows, and who can sit across from clinical operations teams to translate their priorities into actionable, evaluable AI solutions.
Above all, this is a role where the science matters. Every model you ship will eventually touch a trial, and a patient. That is the bar we hold ourselves to, and the bar we hire to.
What you'll do
- Lead AstraZeneca's AI R\&D for clinical operations across Oncology and BioPharmaceuticals — turning AI into measurable cycle\-time, quality, and patient\-experience outcomes in live trials.
- Lead a team of AI researchers and engineers to deliver AI for trial planning, conduct, monitoring, and reporting across the trial lifecycle.
- Partner closely with Clinical Operations to design and implement a strategy for AI\-enabled operations.
- Develop and deploy agentic systems for high\-leverage trial workflows (e.g., AI\-assisted protocol authoring, eligibility screening and patient\-trial matching, site selection, AE and SAE detection, query and data\-review acceleration, source data verification, risk\-based monitoring decision support).
- Build the AI for Clinical Development team's reusable evaluation environments for clinical agents so that every system shipped has a defensible measurement story.
- Oversee the standardization and harmonization of multimodal data for clinical operations AI, including EHR, clinical trial management systems, eTMF.
- Partner with Regulatory teams to develop AI\-enabled trial\-conduct standards and evidence that meet GxP, GCP, data\-integrity, and emerging AI/ML regulatory expectations.
- Work closely with the Heads of AI for Early and Late Clinical Development on AI hand\-offs from design through conduct, and contribute to the team's unified, reusable, and end\-to\-end strategies for AI method development, evaluation, monitoring, and oversight.
- Contribute the AI scientific and methodological voice on regulatory engagement for AI in trial conduct (GCP, agentic systems, data integrity). Represent AstraZeneca externally in industry forums, scientific consortia, and peer\-reviewed venues.
- Serve as a thought partner for AISI and AI for Clinical Development leadership.
- Recruit, mentor, and lead a team of approximately four AI scientists and engineers as a player\-coach who is hands\-on with code, models, and trial\-execution workflows while building a high\-performing team that keeps up\-to\-date with the latest advances in frontier AI models and agents.
Essential for the role
- MD (or equivalent clinical degree), MD\-PhD, or PhD in Computer Science, Machine Learning, Computational Biomedical Sciences, Biomedical Informatics, or a closely related computational discipline, with a strong, hands\-on computational track record.
- Minimum 8 years of combined experience across AI/ML method development and clinical research, clinical operations, or clinical development delivery with demonstrated impact on research enablement or live clinical deployment.
- Current, hands\-on computational expertise: peer\-reviewed publications in top\-tier conferences and/or journals and shipped code in clinical AI, agentic systems, clinical NLP, or related areas. Comfort writing code, reviewing model implementations, and reproducing results.
- Hands\-on experience designing and deploying agentic systems and LLM\-based workflows in real clinical or healthcare environments, including agent harness design, tool\-use scaffolding, multi\-step orchestration, and LLM post\-training methods.
- Strong experience in generative and non\-generative AI benchmarking and evaluation across clinical and/or biomedical settings, including the construction of domain\-specific evaluation environments for tasks with noisy or underspecified ground truth.
- Deep familiarity with modern AI methods relevant to trial operations: large language models, agentic systems, clinical NLP, multimodal models, generative AI, interpretability and oversight, and human\-in\-the\-loop workflow AI.
- Working knowledge of clinical operations and trial conduct and the GxP, GCP, data\-integrity, and validation expectations that apply to AI in live trials.
- Demonstrated track record of translating AI methods into applications that inform clinical decisions or trial execution, including prospective evaluation, deployment, or live\-trial use.
- Demonstrated scientific leadership: mentoring scientists, leading multi\-author projects, or running a small team.
- Excellent written and verbal communication, able to translate technical findings for clinical, regulatory, and executive audiences.
Desirable for the role
- Direct industry experience in clinical operations, clinical trial technology, or digital trial delivery within a pharmaceutical, biotech, or CRO environment.
- Experience with deep\-research, computer\-use, or browsing benchmarks for clinical AI, and the construction of evaluation environments for them.
- Prior FDA, EMA, ICH, or other regulator engagement on AI in trial conduct, GCP for AI, data integrity standards for AI/ML, or evaluation and reporting standards for medical AI.
- First\- or last\-author publications at top ML venues (NeurIPS, ICML, ICLR, \*CL) and/or top clinical and biomedical journals.
- Open\-source contributions, workshop organisation, or standards\-body participation.
Office Working Requirements
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire 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.
The annual base pay for this position ranges from $ 203 213,60 \- 304 820,40 Annual USD. Base pay offered may vary depending on multiple individualized factors, including market location, job\-related knowledge, skills, and experience. In addition, our positions offer a short\-term incentive bonus opportunity; eligibility to participate in our equity\-based long\-term incentive program. Benefits offered include a qualified retirement program \[401(k) plan]; paid vacation and holidays; paid leaves; and, health benefits including medical, prescription drug, dental, and vision coverage in accordance with the terms and conditions of the applicable plans.
\#EA
Date Posted
29\-maj\-2026
Closing Date
12\-juni\-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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Director-level AI roles across all categories have a median of $247,800.
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.
AstraZeneca AI Hiring
AstraZeneca has 8 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Wilmington, DE, US, Gaithersburg, MD, US. Compensation range: $204K - $319K.
Location Context
AI roles in Boston pay a median of $215,350 across 442 tracked positions. That's 8% above the national 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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