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Location Boston, Massachusetts, United States Job ID R\-257858 Date posted 09/08/2026
Executive Director, AI for Discovery
Global Locations
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.
Role Overview
As Executive Director, AI for Discovery within the AI for Science Innovation Unit, you will lead AstraZeneca’s strategy for applying advanced AI and machine learning to accelerate discovery across Oncology and BioPharmaceuticals R\&D. You will drive integration of cutting\-edge AI approaches into target identification, mechanism\-of\-action understanding, compound optimization, and early discovery decision\-making.
You will guide a global organization and serve as a vertical AI capability that connects Discovery Biology, Chemistry, Translational Medicine, Early Development, and Enterprise AI. You will partner closely with scientific and therapeutic area leadership to deliver AI\-enabled insights that improve biological understanding, enhance discovery strategies, and increase probability of technical success across the pipeline.
Key Responsibilities
- Strategic Leadership
- Develop and own AstraZeneca’s enterprise AI strategy for Discovery, spanning target identification/validation, compound optimization, mechanistic modeling, and hypothesis generation.
- Embed AI capabilities across the discovery ecosystem to enable multimodal data integration, mechanistic modeling, and candidate optimization.
- Translate high\-level scientific priorities into a clear portfolio of AI\-enabled discovery capabilities.
- Guide, partner, and lead strategies and initiatives at the highest levels within AstraZeneca Discovery at SET, EVP, and SVP levels.
- Serve as a member of Discovery leadership teams in both ORD and BioPharma R\&D.
Scientific Partnership
- Serve as a thought leader and advisor to Discovery Biology, Discovery Chemistry, Translational Medicine, and Therapeutic Area leadership on AI applications.
- Partner with teams to incorporate AI into experimental design, compound selection, mechanistic studies, and early decision\-making.
- Foster strong relationships across AI for Science Innovation, Enterprise AI, Data Science, and Therapeutic Areas.
Operational \& Organizational Leadership
- Define operating models and talent\-development paths for a world\-class team of AI\-fluent discovery scientists and engineers.
- Shape and manage external partnerships with technology providers and academic labs.
- Represent AstraZeneca externally in scientific and regulatory forums.
- Build and lead a high\-performing global organization promoting rigor, innovation, and collaboration.
- Lead talent development and succession planning for a team of \~20 direct and indirect reports.
Qualifications \& Experience
- PhD, MD, or equivalent in computational, biomedical, chemical, or discovery sciences.
- 10\-12\+ years of experience across discovery science, computational biology, chemistry, or AI/ML in life sciences.
- Track record applying AI to discovery and mechanistic biological challenges.
- Expertise in multimodal data integration, mechanistic modeling, and early discovery analytics.
- Strong scientific publication record in discovery science or AI\-driven biology.
- Experience deploying AI/ML solutions in discovery or early research environments.
- Strong literacy across molecular, multimodal, and language foundation models.
- Executive presence with ability to influence across matrixed organizations.
*The annual base salary for this position in the US ranges from* *$258 157,60 \- $387 236,40**. However, 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) or to receive a retirement contribution (hourly 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 salary (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.\#EAI*
Date Posted
10\-Aug\-2026
Closing Date
25\-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. Director-level AI roles across all categories have a median of $274,554.
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
AI roles in Boston pay a median of $210,000 across 166 tracked positions.
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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