Associate Director, AI Engineering for Discovery
About this opportunity
This is an in-office role based in Barcelona, ES, with a requirement to work a minimum of three days per week on-site. Remote or travel flexibility is not available.
Are you ready to turn cutting-edge AI research into robust, secure products that accelerate how new medicines are discovered? Do you want to build the platforms and workflows scientists rely on to ask bolder questions and make faster, better decisions? In this role, you will lead the engineering that brings high-value AI breakthroughs to life at enterprise scale. You will partner closely with researchers, scientists and platform teams to design, harden and run AI systems that are reproducible, performant and trusted. Your work will shorten the path from idea to impact, helping teams progress promising science with greater speed and confidence. You will operate across cloud and on-premises environments, shaping standards, automation and reusable capabilities that uplift the entire organization. If you thrive on solving hard problems, mentoring others and proving what’s possible with AI in real-world settings, this is the place to make your mark.
Key Accountabilities
Provide technical leadership for AI platform and product engineering, with particular focus on software design, reproducibility, performance and maintainability.
Engineer research prototypes into secure, scalable and supportable products and reusable platform capabilities for Discovery.
Develop and optimize machine-learning training and inference workflows across cloud and on-premises infrastructure.
Own and promote software engineering standards, documentation, testing, code review and reusable delivery patterns.
Use CI/CD, DevOps, GitOps and MLOps automation to improve delivery speed, reliability and operational efficiency.
Partner with AI researchers, scientists, platform teams and external collaborators to translate scientific needs into effective technical solutions.
Mentor engineers, contribute to architecture decisions, and promote responsible, compliant and reproducible AI engineering.
Essential Skills and Experience
A master's degree or PhD in a relevant field
A track-record of implementing software engineering best practices for multiple use cases.
Advanced proficiency in Python and common scientific libraries (e.g. PyTorch, Numpy, Pandas).
Experience with optimization of distributed training of machine learning models.
Experience building and deploying AI/ML systems in production environments.
Experience with GitHub for source control, GitHub Actions, CI/CD, and other MLOps practices.
Experience with deployment of cloud-native applications and use of cloud vendors such as AWS, GCP or Azure.
Excellent problem-solving and technical communication skills.
Demonstrated ability to collaborate effectively across multidisciplinary teams.
Desirable Skills and Experience
Experience implementing Large Language Model (LLM) and Generative AI solutions at enterprise scale.
Experience contributing to architecture design and technical roadmaps.
Experience providing technical leadership on AI or software engineering projects involving responsible AI, governance, security, and reproducibility practices.
Experience with Kubernetes and infrastructure as code.
Understanding of the pharmaceutical industry and its processes.
Experience working in a domain subject to regulatory oversight.
Experience working in scientific research environment.
Here, data, technology and science meet in unexpected ways, computational engineers, clinicians and bench scientists in the same room, unleashing bold thinking that tackles complex disease. You will work with modern tooling and meaningful datasets to build AI capabilities that directly influence research decisions and, ultimately, patient outcomes. We value curiosity alongside rigor, kindness alongside ambition, and we back learning with real opportunities, from experimenting with new approaches to seeing work recognized through publications. With strong collaborations across academia and industry, you can push boundaries while being supported by teams that move quickly, share knowledge and turn promising ideas into tangible progress.
Shape the engineering backbone of discovery, share your CV and tell us about the toughest AI system you’ve taken to production, and take the lead on what comes next!
#EAI
Date Posted
25-sept-2026
Closing Date
06-oct-2026
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. We welcome and consider applications to join our team from all qualified candidates, regardless of their characteristics. We comply with all applicable laws and regulations on non-discrimination in employment (and recruitment), as well as work authorization and employment eligibility verification requirements.
Job details
How this role compares
Computed from every other active Drug Discovery & Preclinical Research role in our database, not just this employer's listings.
We currently track 126 comparable Associate Director Drug Discovery & Preclinical Research roles across 28 biopharma companies.
Salary context
48 of 126 peers report a salary range (USD, annualized)
Peers share this role's job function and a matching or adjacent seniority level -- not necessarily the same therapeutic area or country.
Where these roles are based
Top locations among the 126 comparable roles
+ 4 more countries
Seniority mix
126 of 126 peers have a known seniority level
Therapeutic area mix
10 of 126 peers have a known therapeutic area; the rest are genuinely unlabeled, not hidden
Similar opportunities
The closest matches from our peer group, ranked by how similar they are, not how well you'd qualify for them -- treat this as market context, not a guaranteed shortlist; a weak match is labeled as one below.
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How we calculate "similar"
No black box, no LLM guesswork: a deterministic score built from four normalized attributes. Here's this role's own peer group at different match levels, so you can see the mechanism, not just the result.
Every comparison starts from the same 100-point budget: 25 for working in the same function, 40 for the same therapeutic area, 20 for the same or adjacent seniority, 15 for the same country. A dimension we can't confirm on both sides contributes nothing, never a guess, never a free pass.
0 points, never a partial guess. A role we know almost nothing about beyond its function bottoms out at 25%; it never inflates to 100% just because there's little to compare against. Seniority uses a defined ladder (Associate → Manager → Associate Director → Senior → Principal → Director → Senior Director → Executive/VP) so "Director" and "Senior Director" count as adjacent, but "Director" and "Executive/VP" do not.