Interested in this AI/ML Engineer role at AstraZeneca?
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Location Waltham, Massachusetts, United States Job ID R\-257860 Date posted 06/08/2026
Head of Artificial Intelligence – ICC
Are you ready to build and lead a high\-impact AI organization that turns sophisticated biology into decisive action for patients? Can you unite distributed expertise into a single, strategic engine that accelerates discovery and transforms how we work end to end?
AstraZeneca is creating a new leadership role to consolidate and direct AI across Cell Therapy Discovery and Targeted Immune Engagers. Based in the United States (GTB or BOS), the United Kingdom (Cambridge) or the Netherlands (Amsterdam), you will compose the AI strategy, lead delivery of a high\-value portfolio, and embed AI as a core capability powering our next wave of medicines. As a member of the CTD and TIE leadership teams reporting to the SVP for IO Discovery and Cell Therapy Oncology, you will set direction, mobilize talent, and deliver measurable impact across discovery and operations.
This is a hands\-on, build\-and\-scale mandate. You will form a centralized group of AI experts embedded with R\&D teams, orchestrate initiatives from agentic knowledge hubs to predictive CAR\-T models and in silico binder design, and establish the governance and operating rhythm that turns prototypes into durable platforms and outcomes.
Accountabilities:
\- Strategic Leadership: Define and implement the end\-to\-end AI strategy across CTD and TIE, aligned to enterprise AI goals, with a clear roadmap for 2026–2027 and beyond.\- Portfolio Orchestration: Prioritize and deliver a focused slate of initiatives including agentic knowledge hubs, predictive modeling for cell therapy, in silico protein and binder design, TCR affinity maturation, CRISPR off\-target safety, and next\-generation analytics.\- Agentic AI Development: Build, test, and scale knowledge hub capabilities that enable collaborative analysis, rapid retrieval of institutional knowledge, and faster, better decisions.\- Predictive Modeling for Cell Therapy: Lead models that optimize CAR\-T design and performance, reducing cycle times from hypothesis to validation and improving program selection.\- In Silico Protein and Binder Design: Deploy AI workflows that generate and refine binders and mature affinity, increasing hit quality and reducing experimental burden.\- CRISPR Safety and Risk: Implement sophisticated off\-target workflows to improve safety assessments, strengthen study build, and de\-risk pipelines.\- Workflow Automation: Automate research and analytics processes to streamline operations, reduce manual effort, and increase reproducibility across sites and teams.\- AI Upskilling and Culture: Orchestrate training that lifts foundational AI literacy and fosters an innovative, high\-integrity culture where scientists and engineers co\-create solutions.\- Collaborator Partnership: Build deep collaboration with enterprise AI, platform, and external partners to align standards, share knowledge, and improve resource leverage.\- Governance and Value Realization: Implement robust governance, regulatory compliance, and budget/resource management; institute KPIs that quantify scientific and operational value.\- Communication and Influence: Translate sophisticated technical insights into clear narratives for executive and non\-technical collaborators, shaping R\&D strategy and investment decisions.
Essential Skills/Experience:
\- Advanced degree (Master’s or PhD) in Computer Science, Engineering, Mathematics, or a related quantitative field.\- Demonstrated 10\+ years of experience successfully leading high\-performing AI teams and sophisticated AI programs, ideally in life sciences, technology, or R\&D\-driven environments.\- Strategic skill in shaping, scaling, and transforming AI activities for maximum business and scientific impact.\- Expertise in the development and deployment of AI/ML technologies, with proven outcomes in sophisticated, multi\-stakeholder environments.\- Strong understanding of biology or R\&D workflows preferred but not required; ability to translate between technical and scientific teams is essential.\- Outstanding organizational, communication, and collaborator engagement skills, including experience communicating/translating sophisticated technical findings and priorities to executive and non\-technical partners.\- Proven experience building, mentoring, and scaling multi\-disciplinary teams comprised of machine learning scientists, AI engineers, and data professionals, distributed across multiple locations and embedded in different R\&D teams.\- Track record of encouraging a collaborative, innovative, and high\-integrity team culture.
Desirable Skills/Experience:
\- Direct experience applying AI/ML to cell therapy, protein engineering, immunology, or related modalities.\- Demonstrated delivery of one or more: agentic knowledge hubs, CAR\-T predictive models, in silico binder generation, TCR affinity maturation workflows, CRISPR off\-target analyses, or computational mutagenesis.\- Familiarity with LLMs, knowledge graphs, MLOps, and cloud\-native platforms; experience integrating these into enterprise environments.\- Experience with data governance, model risk management, and compliance practices relevant to R\&D and regulated settings.\- Success managing multi\-site teams and external ecosystems, including vendors, consortia, and academic collaborations.\- Portfolio management experience with clear KPI frameworks and budget ownership.
When we put unexpected teams in the same room, we unleash ambitious thinking with the power to encourage 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 outstanding and ambitious world!
Why AstraZeneca:
Here, ground breaking science meets ambitious technology in service of patients. You will join a community that thrives on collaboration across subject areas, where ideas move quickly from exploration to scaled platforms that change how we discover and develop medicines. We put unexpected teams together to unlock new thinking, invest deeply in digital capabilities across the R\&D lifecycle, and value kindness alongside ambition so people can take smart risks and learn fast. Your leadership will connect AI breakthroughs to tangible outcomes for patients and programs, while opening new horizons for your own growth.
Date Posted
07\-Aug\-2026
Closing Date
21\-Aug\-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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
AstraZeneca AI Hiring
AstraZeneca has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Cambridge, MA, US, Gaithersburg, MD, US, Boston, MA, US. Compensation range: $216K - $216K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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