Associate Director, AI Engineering for Discovery

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About the role

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 d

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