Interested in this AI/ML Engineer role at AstraZeneca?
Apply Now →Skills & Technologies
About This Role
Location Cambridge, Massachusetts, United States Job ID R-260990 Date posted 30/09/2026
Are you a forward-thinking and curious**Translational Data Scientistwith deep expertise insingle-cellandspatial omics and a passion for applying AI to biological discovery? Do you want to help shape the future of**Immunology and Cell Therapy**by generating insights that accelerate the discovery and development of transformative medicines? Welcome to AstraZeneca.
We are looking for an experienced Translational Data Scientist to join our Data Science and Bioinformatics team.
You will be part of the Translational Medicine and Bioinformatics department in the Immunology therapeutic area. In this role, you will work in a highly collaborative and science-driven environment focused on advancing our understanding of immune-mediated disease biology, identifying novel therapeutic opportunities, and enabling precision approaches across our Immunology portfolio from discovery through clinical development.
As a department we promote an open and collaborative atmosphere, with a culture that is both science-based and patient-focused. Our mission is to understand disease mechanisms at the molecular and cellular level, identify the most effective targets, biomarkers, and patient subgroups, and support translation of scientific discoveries into medicines that improve patients’ lives.
This role is located at our site in Kendall Square, Boston.
Be encouraged and supported to think creatively in our dynamic environment. Here, we are free to ask the right questions and make bold decisions. We harness multi-omics, computational biology, and AI to accelerate research, connect complex molecular and clinical data to key biological questions, and ensure that discoveries made in the lab can make a meaningful difference for patients.
What you will do
In this role, you will provide scientific leadership in a highly cross-functional and thriving environment together with specialists in immunology, cell therapy, translational medicine, and data science, as well as with other translational data scientists and AI experts.
We are looking for an outstanding Translational Data Scientist to lead the integration and interpretation of large-scale multimodal datasets, with particular emphasis on single-cell and spatial omics in immunology. You will define innovative analytical and AI-driven approaches to identify disease mechanisms, therapeutic hypotheses, biomarkers, and patient subgroups, helping to support decision-making across the portfolio.
You will lead the development of transformational precision medicine propositions to deliver life-changing medicines to patients. You will be responsible for planning, performing, interpreting, and communicating analyses that maximize the value of our growing data assets. These may include single-cell transcriptomics, spatial transcriptomics, BCR/TCR repertoire data, high-dimensional immune profiling data, and other complementary molecular and clinical datasets.
You will provide scientific leadership in the evaluation, adoption, and practical application of AI and machine learning methodologies across the team. This includes hands-on experience with large language models, biological foundation models, and other frontier AI approaches applied to multimodal biological data analysis, knowledge extraction, and translational insight generation. Critically, you will act as a scientific guide and quality arbiter for AI outputs; ensuring biological validity and translational relevance rather than simply a consumer of AI tools.
By applying state-of-the-art computational methods and providing scientific leadership in a collaborative cross-functional setting, you will help generate actionable biological insight in immunological diseases and support the advancement of next-generation therapeutic strategies, including cell therapy.
Essential for the role
- Relevant PhD (or equivalent) in computational biology, translational data science, bioinformatics, biostatistics, or a related quantitative discipline, plus at least 6+ years of relevant applied experience in academia, biotech, or pharmaceutical R&D.
- Substantial experience in Immunology and/or Cell Therapy research within a pharmaceutical, biotech, or leading academic environment. with evidence of shaping translational strategy or influencing programme direction.
- Deep expertise in single-cell omics analysis, with strong hands-on experience in developing and applying advanced approaches to single-cell RNA-seq data and translating the results into meaningful biological or therapeutic insights.
- Strong experience in spatial omics / spatial transcriptomics / spatial proteomics.
- Expertise in at least one of BCR/TCR repertoire analysis or high-dimensional cytometry data analysis such as CyTOF, together with interest in working across both.
- Proven experience in multimodal data integration across molecular and clinical datasets, including defining integration strategies, selecting fit-for-purpose methods, and interpreting evidence across modalities such as single-cell transcriptomics, spatial omics, immune repertoire data, cytometry, and associated metadata.
- Excellent coding skills in Python, R, or similar languages appropriate for large-scale omics analysis, together with experience in version control such as Git/Bitbucket, Linux environments (cluster and/or cloud), and effective use of modern AI-assisted coding tools such as GitHub Copilot or Claude Code.
- Expertise in immunology and the use of omics data to derive biological insight relevant to disease mechanisms, translational research, biomarker strategies, and therapeutic development.
- Demonstrated hands-on experience applying machine learning and AI methods to biological data, including familiarity with at least one of: biological foundation models (e.g. scGPT), deep learning frameworks (PyTorch or TensorFlow), large language models for biological knowledge extraction or agentic workflows, or generative and representation learning approaches.
- Ability to critically evaluate AI tool outputs in a biological and translational context.
- Outstanding communication, collaboration and influencing skills, with the ability to explain complex computational analyses to both expert and non-expert audiences, provide clear recommendations to senior stakeholders, constructively challenge scientific assumptions, and build alignment across cross-functional teams.
- A high degree of scientific independence, strategic judgement, curiosity, and a proactive, delivery-focused mindset.
Desirable for the role
- Experience with both BCR/TCR repertoire analysis and CyTOF.
- Experience working within AI-augmented analytical pipelines and the ability to define when and where AI adds genuine scientific value versus where human biological judgement remains essential.
- Track record of applying translational data science and computational analyses to drive decisions in drug discovery, translational science, or clinical development.
- Experience leading projects and collaborating with internal and/or external partners in matrixed, multidisciplinary teams.
Why AstraZeneca?
At AstraZeneca we turn ideas into life changing medicines and strive to continuously meet the unmet needs of patients worldwide. Working here means being entrepreneurial, thinking big, and working together to make the impossible a reality. If you are swift to action, confident to lead, willing to collaborate, and curious about what science can do, then you’re our kind of person.
Work policy
When we put unexpected teams in the same room, we unleash bold thinking with the power to inspire life-changing medicines. In-person working give 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.
What’s next?
If you are ready to make a difference - apply today, and we'll make it happen together!
Diversity & Inclusion
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. All qualified applicants will receive consideration for employment without regard to race, colour, religion, sex, sexual orientation, gender identity, national origin, disability, or protected veteran status.
How to Apply
Please submit your CV together with a short cover letter outlining your relevant experience and motivation for the role. Applications will be reviewed on a rolling basis.
The annual base pay for this position ranges from $143,323.20 - $214,984.80. 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
01-Oct-2026 Closing Date
23-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 $143K-$214K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 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 $212,000 based on 11,857 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,275. This role's midpoint ($179K) sits 15% below the category median. Disclosed range: $143K to $214K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
AstraZeneca AI Hiring
AstraZeneca has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Gaithersburg, MD, US, Cambridge, MA, US, London, England, UK. Compensation range: $108K - $214K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.