Director of Machine Learning Research - AI Applications

This position is posted by Jobgether on behalf of a partner company. We are currently looking for a Director of ML Research - AI Applications in Switzerland.

This is a senior technical leadership role at the forefront of applying machine learning to pharmaceutical R&D, with a strong focus on structural biology and drug discovery. You will be responsible for building and leading a newly created ML Research function within an AI Applications organization, shaping its scientific direction while remaining deeply hands-on in model development and experimentation. The role combines cutting-edge research, team leadership, and real-world deployment of production-grade AI models. You will work across federated data networks, collaborating with engineering, product, and academic partners to translate scientific breakthroughs into scalable solutions. A key focus will be improving co-folding models and advancing their generalization across complex biological datasets. This is a player-coach position for a leader who thrives at the intersection of research excellence and applied impact in drug discovery.

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

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• 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

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• 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.
  
  

How Jobgether Works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.