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About This Role
Location Boston, Massachusetts, United States Job ID R\-253548 Date posted 01/06/2026
About AISI
AI Strategy \& 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 an AI Engineer to build, post\-train, evaluate, and deploy the next generation AI and agentic systems that power AstraZeneca's trials.
AI for clinical development is a field in motion. Foundation models, multimodal learning, agentic systems, post\-training methods, and evaluation science evolve rapidly and the regulatory and methodological frameworks around them are evolving in parallel. You'll identify and implement new methods as the field redefines what's possible. Comfort with ambiguity, an instinct to learn, and a bias for shipping are core to the role.
The AI for Clinical Development function is being built from the ground up. As a lead engineer, you'll have unusual influence on the technical decisions, the eval and infrastructure choices, and the shape of the work. You’ll also have direct exposure to the regulators, scientific consortia, and external partners writing the rules of the road for AI in clinical evidence.
We're looking for someone who can take a clinical problem from sketch, through model and agent design, through eval, and back through the feedback loop, and who understands that every model that ships as something that will eventually touch a trial, and a patient.
What you'll do:
- Build the next generation of agentic environments for AstraZeneca's clinical development workflows.
- Lead the team's frontier AI safety and oversight work.
- Collaborate across research and infrastructure teams to develop and ship environments.
- Debug and iterate rapidly across research AI/ML stacks.
- Contribute to research culture through technical discussions and collaborative problem\-solving across AI and clinical teams.
- Train/post\-train models for clinical alignment.
- Design agent harnesses, tool\-use scaffolding, and multi\-step orchestration for high\-leverage clinical\-development workflows.
- Build domain\-specific evaluation environments and benchmarks for frontier clinical AI with end\-to\-end tracing, automated trace\-level scoring, and drift/oversight detection.
- Partner with clinical development domain experts including clinicians, biostatisticians, translational scientists, clinical operations, and regulatory to scope problems, ship prototypes, and iterate on real\-world feedback.
- Contribute to the team's reusable infrastructure for evaluation, monitoring, and oversight of AI in clinical development.
- Publish at top ML venues and top clinical and biomedical journals to maintain scientific credibility and external recruiting pull.
- Mentor more junior engineers and scientists, and represent AstraZeneca externally in industry forums and scientific meetings.
Essential for the role
- PhD in Computer Science, Machine Learning, Computational Linguistics, Biomedical Informatics, or a closely related computational discipline; or MSc with equivalent industry experience with large language models, agentic systems, and their evaluation.
- Minimum 3 years demonstrated, hands\-on expertise in modern LLMs, multimodal models, and agentic systems, including end\-to\-end post\-training, tool\-use and agent harness design, and inference / serving in production.
- Excellent software engineering skills: Python, PyTorch, Hugging Face, cloud platforms (e.g., AWS, Azure, GCP), and modern LLM tooling.
- Track record of building and shipping AI systems that have been deployed in real clinical, healthcare, or other high\-stakes domains.
- Expertise in sandboxing, containerization, VM infrastructure, and/or distributed systems.
- Strong experience constructing and using domain\-specific evaluation environments and benchmarks for clinical or biomedical AI.
- Experience with AI safety and responsible AI methods applied to clinical AI.
- First\- or co\-first\-author publications at top ML venues (NeurIPS, ICML, ICLR, \*CL).
- Excellent written and verbal communication, and ability to translate technical findings for clinical, regulatory, and executive audiences.
- Eager to work across the ML / clinical / operations / regulatory boundary.
Desirable for the role
- Direct experience with clinical NLP and clinical information extraction.
- Experience constructing hard, useful clinical benchmarks relevant to modern AI methods.
- Open\-source contributions, leadership on widely\-used clinical AI artifacts (datasets, benchmarks, models, evaluation tooling), and/or workshop and standards\-body participation (e.g., Clinical NLP Workshop, ML4H, GenAI4Health, ML / clinical reporting standards).
- Prior engagement with FDA, NCI, NIH, or other regulators or public\-health bodies on AI methodology, evaluation standards, or AI safety.
- Mentorship of junior engineers/computer scientists.
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 $186,014\.40 \- $279,021\.60 USD Annual. Hourly and salaried non\-exempt employees will also be paid overtime pay when working qualifying overtime hours. 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 (salaried roles), to receive a retirement contribution (hourly roles), and commission payment eligibility (sales roles). Benefits offered included 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. Additional details of participation in these benefit plans will be provided if an employee receives an offer of employment. If hired, employee will be in an “at\-will position” and the Company reserves the right to modify base pay (as well as any other discretionary payment or compensation program) at any time, including for reasons related to individual performance, Company or individual department/team performance, and market factors.
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.
Equal Opportunities Statement
AstraZeneca embraces diversity and equality of opportunity. We are committed to building an inclusive and diverse team representing all backgrounds, with as wide a range of perspectives as possible, and harnessing industry\-leading skills. We believe that the more inclusive we are, the better our work will be. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non\-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
Date Posted
02\-Jun\-2026
Closing Date
08\-Jun\-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: $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 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. This role's midpoint ($232K) sits 28% above the category median. Disclosed range: $186K to $279K.
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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