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About This Role
Location Gaithersburg, Maryland, United States Job ID R-262275 Date posted 19/10/2026
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 Summary
AstraZeneca is building a world-class AI for Clinical Development capability within the Enterprise AI Unit. It will accelerate the design, conduct, and analysis of clinical trials across our enterprise pipeline. We are seeking an AI Senior Specialist, Clinical AI Delivery Operations, to build and run the operational and method delivery foundation that connects clinical AI research with clinical development and patient impact.
You will make sure that multimodal data, models, and AI outputs are fit for purpose for AI development, scientific and clinical decision-making, and regulatory use. You will also make sure that delivery, regulatory, compliance, and quality standards are met from the start of each project.
You will also develop the team-wide operating processes and governance standards that make AI system and output delivery consistent, auditable, and scalable. You will work with AI scientists and engineers, clinical and regulatory domain experts, IT, data partners, and quality partners.
This team is being built from the ground up. The ideal candidate is organized, dependable, data-driven, and detail-oriented. They are motivated by translating advanced and emerging AI research into governed, compliant methods and processes designed for the highly regulated clinical development and operations settings.
Key Responsibilities
Team Processes and Governance Standards
- Define, implement, and maintain team-wide standards, SOPs, templates, and checklists for data and model intake, curation, dataset release, documentation, and handover.
- Own the team's approach to data and model governance and quality. This covers dataset versioning, lineage and traceability, fit-for-purpose criteria, and quality control.
- Serve as the point of contact for governance, compliance, and quality questions. Keep the team aligned with internal policies and external expectations as they evolve.
- Establish metrics for delivery performance, data and model quality, and compliance, and report on them to team leadership.
- Support audit and inspection readiness for the team's data and AI workflows.
Data Access, Transfer, and Curation
- Coordinate with enterprise data owners, clinical data management, IT, and external collaborators to access and transfer multimodal data, models, and associated metadata.
- Ensure datasets and models meet defined formats, metadata standards, and project requirements for each modality.
- Translate scientific and model development requirements into structured dataset preparation tasks.
- Verify the completeness, accuracy, and quality of datasets in line with SOPs.
- Maintain consistency, traceability, and usability of datasets across projects.
Delivery Operations for Clinical AI
- Run and coordinate the operational workflows that take clinical AI work from research to evidence-backed output. This includes data pooling, subsetting and splitting, annotation consolidation, and reproducible data preparation.
- Support handover of validated datasets, models, and outputs to clinical, regulatory, engineering, and IT partners.
- Manage storage and release of results in approved data environments.
Regulatory, Compliance, and Quality
- Ensure datasets and workflows comply with internal policies and governance frameworks. Examples are data privacy, human biological sample governance, and data access approvals (e.g., iDAP).
- Initiate and coordinate approvals with the Data Enablement, Quality, IT, and Compliance teams.
- Build in the data integrity and audit-readiness expected for regulated work.
- Author and maintain documentation such as data collection plans, data and model lineage records, analysis plans, and reports.
- Manage QC checks, deviations, and issue resolution.
Cross-Functional Collaboration
- Work closely with AI scientists, ML engineers, data scientists, program managers, clinical and regulatory business partners, IT and data teams.
- Communicate clearly about data and model readiness, limitations, and uncertainties to support decisions.
Operational Excellence and Continuous Improvement
- Identify and implement improvements to workflows, tooling, and processes so that delivery scales as the team grows.
- Stay informed about emerging data and model management practices, clinical and multimodal data and model standards, and AI governance expectations.
Desired Profile
Education
- Bachelor's or Master's degree in data science, bioinformatics, health or biomedical informatics, or a related field.
- Experience in clinical data management, life sciences, or healthcare is highly desirable.
Experience
- 5 years of experience managing complex scientific or clinical datasets, including multimodal data.
- Experience defining or owning data and/or model management processes, standards, or governance frameworks, ideally in a regulated environment.
- Fluency with FAIR data principles and their application in life sciences.
- Working knowledge of cloud-based data environments, modern data platforms, and dataset management workflows.
- Understanding of AI/ML data requirements for model development, validation, and monitoring.
- Experience with dataset versioning, reproducible data preparation, and quality control.
- Working knowledge of Python or a similar scripting language for data preparation, automation, and QC.
- Familiarity with data governance, compliance processes, and controlled handling of protected data.
- Proven ability to coordinate delivery across multiple stakeholders and workstreams.
- Familiarity with generative AI or agentic systems in clinical or regulatory settings.
- Experience with MLOps, data pipelines, or model lifecycle documentation.
Desirable:
- Knowledge of clinical and regulatory data standards (e.g., CDISC SDTM/ADaM, eCTD, DICOM) and of GxP computerized system validation.
- Experience supporting audits or inspections.
- Working knowledge of Atlassian tools (JIRA, Confluence).
Key Competencie
- Excellent interpersonal, verbal, and written communication skills in an international matrix environment.
- Ability to influence without authority and to gain adoption of standards across scientific and engineering teams.
- Strong execution mindset, with ownership and accountability for high-quality delivery.
- Highly structured and detail-oriented, with strong follow-through.
- Pragmatic, fit-for-purpose problem solving, especially when diagnosing data quality or workflow issues.
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 [80% - 120% ]. 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.
Why AstraZeneca:
Here you will fuse cutting-edge science with advanced AI to move a robust pipeline forward across multiple indications, including programs that aim to change the course of cancer. We bring surprising combinations of experts together—biostatisticians with AI engineers, clinicians with product leaders—to make faster, smarter decisions that accelerate breakthrough medicines. Backed by significant investment and a culture that values kindness alongside ambition, you will be trusted to lead, challenged to think boldly, and supported to turn ideas into real-world impact for patients.
Call to Action:
If you are ready to turn strategic intent into scaled AI that transforms clinical development and speeds medicines to patients, we want to hear how you will lead this next chapter—send your application today!
Date Posted
20-Oct-2026 Closing Date
03-Nov-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 2094 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 10,827 AI roles we're tracking, AI/ML Engineer positions make up 77% 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 $212,160 based on 13,809 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($232K) sits 10% above the category median. Disclosed range: $186K to $279K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
AstraZeneca AI Hiring
AstraZeneca has 10 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Gaithersburg, MD, US, Gaithersburg, MD, Cambridge, MA, US. Compensation range: $206K - $279K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 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 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 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 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). 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 (122) are outnumbered by mid-level (5,256) and senior (3,922) 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 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 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 $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. 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 $266,050 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 (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 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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