Technical Lead – Large Molecule AI Systems
This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Technical Lead – Large Molecule AI Systems in United Kingdom.
This role sits at the intersection of artificial intelligence, structural biology, and large-scale pharmaceutical research, focusing on the development of next-generation AI systems for biologics discovery. You will lead the delivery of complex machine learning programs applied to antibody modeling, protein folding, and developability prediction, transforming advanced scientific research into robust, production-ready systems. Working within a highly collaborative, research-driven environment, you will guide multidisciplinary teams of ML engineers and scientists, ensuring that experimental models evolve into reliable, scalable solutions used in real-world drug discovery workflows. The role requires balancing strategic technical leadership with hands-on contribution, particularly in model design, evaluation, and system architecture. You will also play a key role in aligning scientific stakeholders around clear objectives, timelines, and measurable outcomes. This is a high-impact position where your work directly accelerates innovation in pharmaceutical R&D through federated AI systems.
Accountabilities:
- Lead the development and delivery of federated large molecule AI systems across domains such as antibody modeling, protein folding, binder prediction, and biologics developability.
- Drive the implementation of large-scale biomolecular foundation models, including systems inspired by OpenFold, Boltz-2, and ESM, ensuring reliable and high-quality model releases.
- Translate ambiguous scientific and technical goals into structured execution plans, prioritization frameworks, and clearly defined workstreams.
- Define evaluation strategies, validate model performance, and ensure outputs meet production-grade standards for real-world drug discovery applications.
- Manage risks, dependencies, technical trade-offs, and delivery timelines, providing clear recommendations to stakeholders and leadership.
- Align consortium and cross-functional stakeholders on data requirements, objectives, evaluation criteria, and delivery expectations.
- Collaborate closely with product, engineering, research, and leadership teams to ensure model roadmaps align with application and business needs.
- Act as a player-coach, contributing directly to modeling, experimentation, and architecture decisions while mentoring senior engineers and ML scientists.
Benefits:
- Competitive compensation package including virtual share options.
- Fully remote-first working model with flexibility across locations.
- Wellbeing budget and access to mental health support services.
- Home office setup allowance and co-working space stipend.
- Dedicated learning and development budget for courses, conferences, and certifications.
- Generous holiday allowance to support work-life balance.
- Periodic in-person collaboration sessions in European locations, including Berlin HQ.
- Opportunity to work with a high-caliber, execution-focused team operating at the forefront of AI and drug discovery innovation.
Requirements:
- Advanced degree (PhD, MSc, or equivalent experience) in machine learning, computational biology, structural biology, or a related field.
- 5+ years of experience applying machine learning to complex biological or scientific problems such as antibody engineering, protein design, binder prediction, or drug discovery.
- Strong hands-on expertise in Python and PyTorch, with experience working on or extending large-scale models such as AlphaFold, OpenFold, Boltz, or ESM.
- Proven experience in ML system delivery, including evaluation, training, deployment, and validation of production-ready models.
- Solid understanding of ML infrastructure and MLOps practices, including Kubernetes-based training and distributed workflows.
- Demonstrated ability to lead end-to-end ML projects, define technical direction, and drive teams toward high-quality delivery outcomes.
- Strong capability to define success metrics, validate model quality, and ensure robustness for real-world applications.
- Experience operating in cross-functional environments involving research, engineering, product, and scientific stakeholders.
- Excellent communication skills and ability to turn ambiguous scientific challenges into clear, executable technical plans.
- Experience in federated learning, distributed training, or privacy-preserving ML is considered a strong advantage.
- Prior exposure to regulated, enterprise, pharmaceutical, or biotech environments is a plus.
- Publications in top-tier ML or computational biology venues are considered a strong asset.
