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Location Gaithersburg, Maryland, United States Job ID R\-256166 Date posted 08/07/2026
Make a meaningful impact on patients’ lives around the world
At AstraZeneca, we are combining cutting\-edge science, data and artificial intelligence to transform how we discover and develop medicines. This role sits within the Centre for Genomics Research (CGR) and works in close collaboration with AstraZeneca Infectious Disease and Oncology, offering a unique opportunity to shape new digital and AI capabilities for the cancer vaccine space.
Based in CGR, you will sit at the intersection of mass spectrometry\-based proteomics, immunopeptidomics and agentic AI — building the data foundations and intelligent systems that allow scientists to explore high\-throughput proteomics, HLA\-peptidomics, neoantigen discovery, genetic sequencing, transcriptomics and emerging molecular assays as a connected whole. This is a hands\-on technical role for someone who can combine deep proteomics expertise with the practical engineering judgement needed to design, deploy and scale AI systems that scientific users actually rely on.
What you'll do
As Associate Director, Digital Proteomics and AI Innovation within CGR, you will:
- Identify high\-value workflow opportunities where agentic AI can transform how proteomic, peptidomic and multi\-omic data are explored, integrated, interpreted and automated.
- Design, build, deploy and iterate AI\-enabled systems — including agentic workflows — that are usable, maintainable and integrated into real scientific practice.
- Bring deep domain expertise in mass spectrometry, proteomics and peptidomics to shape data standards, analytical approaches and biological interpretation, with particular relevance to immunopeptidomics and neoantigen\-centric workflows.
- Drive integrative analysis across proteomics, peptidomics, sequencing, transcriptomics and experimental metadata to generate translational insight.
- Partner with informaticians, data and mass spectrometry scientists within CGR, and collaborate closely with scientific stakeholders in AstraZeneca Infectious Disease and Oncology, to embed AI capabilities into scalable, governed analytical environments.
- Contribute technical expertise to scientific discussions, sharing knowledge with colleagues and engaging with internal and external scientific communities.
Essential criteria
- Advanced degree (PhD or equivalent experience) in proteomics, computational biology, bioinformatics, data science, biostatistics, computer science or a related discipline.
- Deep expertise in mass spectrometry\-based proteomics and peptidomics, including a working understanding of experimental design, data characteristics, limitations and interpretation challenges.
- Strong hands\-on data science and software delivery experience, with a track record of building, deploying and maintaining analytical or AI\-enabled systems used by scientific teams.
- Demonstrated experience developing agentic AI workflows or advanced AI systems for scientific data exploration, interpretation or workflow automation.
- Proven ability to integrate and analyse multidimensional datasets spanning proteomics, peptidomics, sequencing, transcriptomics and associated metadata to generate biological insight.
- Ability to operate as an independent, hands\-on technical contributor — combining scientific judgement with pragmatic engineering and delivery.
- Excellent communication and collaboration skills, with a track record of translating complex scientific and technical concepts into solutions that scientific and technical users adopt.
Desirable criteria
- Experience applying digital proteomics and AI\-enabled approaches in oncology, immunology, cancer vaccines, immunopeptidomics or neoantigen discovery.
- Experience designing data foundations, ontologies, harmonised analytical environments or reusable AI capabilities for large\-scale scientific research.
- Experience extending across additional omics modalities in partnership with domain specialists.
- Track record of innovation evidenced through deployed tools, software products, open\-source contributions, peer\-reviewed publications or conference presentations in proteomics, peptidomics, AI or data science.
How we do it
At AstraZeneca, we're dedicated to being a Great Place to Work, where you are empowered to push the boundaries of science and unleash your entrepreneurial spirit. There's no better place to make a difference in medicine, patients, and society. An inclusive culture that champions diversity and collaboration. Always committed to lifelong learning, growth, and development.
Date Posted
09\-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 in Demand for This Role
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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