Technical Lead – Large Molecule AI Systems

This is a fully remote position, open to applicants in Europe.

📋 Description

• Lead teams in the development and delivery of federated large molecule AI systems, maintaining a hands-on approach in antibody modeling, co-folding, binder prediction, and developability.

• Design and implement machine learning applications for large biomolecular foundation models such as OpenFold, Boltz-2, and ESM.

• Take ownership of delivering these projects against established milestones, ensuring that high-quality model releases are completed on schedule.

• Convert vague scientific and technical objectives into coherent plans, priorities, workstreams, and decisions.

• Facilitate evaluation decisions and leverage them to provide results packages to external stakeholders.

• Proactively identify risks, blockers, bugs, timeline alterations, and technical trade-offs early, along with clear recommendations.

• Align consortium participants on goals, evaluation criteria, data needs, timelines, and delivery expectations.

• Collaborate with product, engineering, research, and leadership teams to ensure application requirements influence the model roadmap.

⛳️ Requirements

• PhD, MSc, or equivalent experience in a relevant discipline.

• Over 5 years of experience applying machine learning to intricate scientific or biological challenges, preferably in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design.

• Practical experience with contemporary ML systems in Python and PyTorch.

• Experience working with or enhancing large-scale models like OpenFold, AlphaFold, Boltz, ESM, or similar.

• Experience in MLOps or ML infrastructure, specifically with Kubernetes-based training, evaluation, or deployment workflows.

• Establish success criteria, validate model quality, and ensure that ML releases are robust for real-world applications.

• Led the delivery of complex ML projects, including setting technical direction, managing risks and dependencies, and guiding teams towards high-quality releases.

• Comfortable functioning as a player-coach: mentoring engineers and ML scientists while directly contributing to modeling, experimentation, or architecture as needed.

• Collaborate effectively with product, research, leadership, customers, and scientific stakeholders to transform ambiguous requirements into clear technical plans.

🏝️ Benefits

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

• Remote-first work environment – choose where you work best.

• Wellbeing budget, mental health support, work-from-home budget, co-working stipend, and learning budget.

• Generous holiday allowance.

• Office Days at our Berlin headquarters or another European location (three times a year).

• A high-caliber, execution-focused team with experience from leading organizations.