Director of ML Research – AI Applications

Accountabilities:

• Establish and lead the dedicated ML Research team within the AI Applications organization, defining its scientific vision, research mandate, and long-term direction.

• Drive the design, training, and improvement of large-scale foundation models for structural biology, with a focus on co-folding and protein interaction modeling.

• Develop and refine data pipelines and model architectures using large proprietary datasets, incorporating geometric and physical priors for improved biological accuracy.

• Translate cutting-edge research in machine learning and structural biology into practical, production-ready modeling approaches for drug discovery applications.

• Lead hands-on experimentation, model evaluation, and applied research workstreams, particularly around co-folding model generalization and regularization.

• Collaborate closely with engineering, product, privacy, and domain teams to ensure seamless integration of research outputs into production systems.

• Partner with academic institutions and research labs, contributing to publications and presenting findings at leading scientific conferences.

• Represent the organization in customer discussions and scientific forums, addressing complex modeling challenges across pharma partners.

• Build, mentor, and grow a high-performing ML research team over time.

Requirements:

• PhD or MSc in Computer Science, Machine Learning, Computational Biology, or a related field, with 7+ years of relevant experience including 3+ years in technical leadership.

• Strong expertise in applying machine learning to biological problems, particularly structural biology (e.g., co-folding, protein modeling) or related domains such as ADMET.

• Proven publication record in top-tier ML or computational biology venues (e.g., NeurIPS, ICML, ICLR, ISMB, RECOMB, or equivalent).

• Hands-on experience with modern ML frameworks such as Python and PyTorch, and familiarity with large-scale models (e.g., OpenFold, Boltz, or similar).

• Proven ability to operate as a player-coach, combining technical leadership with direct contribution to modeling and experimentation.

• Strong experience working across cross-functional and customer-facing environments, translating complex scientific problems into actionable technical approaches.

• Ability to thrive in ambiguous, research-driven environments with a strong applied focus.

• Nice to have: experience in early-stage biotech, building ML research functions from scratch, or working with distributed training across GPU/cloud platforms (AWS, Azure, Lambda).

• Experience with ML infrastructure and MLOps, including Kubernetes-based workflows.

• Familiarity with QSAR modeling approaches, Triton kernel optimization, or system-level ML performance tuning.

• Exposure to federated learning, privacy-preserving ML, or multi-party training environments.

Benefits:

• Competitive industry compensation package, including early-stage virtual share options.

• Remote-first working model with flexibility to work from anywhere.

• Wellbeing budget, mental health support, home office allowance, co-working stipend, and learning budget.

• Generous holiday entitlement.

• Regular in-person company gatherings at Berlin HQ or other European locations (approximately three times per year).

• Opportunity to work with a highly experienced, execution-focused team from leading organizations.

• Exposure to cutting-edge AI research applied directly to pharmaceutical drug discovery challenges.

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