[Hiring] Technical Lead - Structural Biology Networks @Apheris

Role Description

We are looking for a technical lead to own delivery of our AI Structural Biology model programs. This is a hands-on leadership role at the intersection of foundation models, structural biology, and federated learning. You will turn ambitious scientific goals into reliable model systems that can be evaluated, released, and used in real drug discovery workflows.

• Set technical direction, drive execution, and challenge modeling decisions.
• Turn ambiguity into executable plans while managing risks and dependencies.
• Mentor senior engineers and ML scientists, getting into technical depth when needed.
• Lead teams building and delivering federated co-folding models.
• Build and implement ML applications in structural biology, particularly around fine-tuning and extending foundational models like OpenFold, Boltz-2, and ESMFold.
• Own delivery against committed milestones and ensure high-quality model releases ship on time.
• Translate ambiguous scientific and technical goals into clear plans, priorities, workstreams, and decisions.
• Guide evaluation decisions and build on them to deliver results packages to external stakeholders.
• Surface risks, blockers, bugs, timeline changes, and technical trade-offs early, with clear recommendations.
• Align consortium members on objectives, evaluation criteria, data requirements, timelines, and delivery expectations.
• Work with product, engineering, research, and leadership to ensure application requirements shape the model roadmap.

Qualifications

• PhD, MSc, or equivalent experience in a relevant field.
• 5+ years applying ML to complex scientific or biological problems, ideally in structural biology, protein modeling, co-folding, or binding prediction.
• Hands-on experience with modern ML systems in Python and PyTorch.
• Experience with or extended large-scale models such as OpenFold, AlphaFold, Boltz, ESM, or similar.
• MLOps or ML infrastructure experience, particularly with Kubernetes-based training, evaluation, or deployment workflows.
• Experience leading delivery of complex ML projects, including setting technical direction and managing risks and dependencies.
• Comfortable operating as a player-coach: mentoring engineers and ML scientists while contributing directly to modeling, experimentation, or architecture when needed.
• Ability to work effectively with product, research, leadership, customers, and scientific stakeholders to turn ambiguous requirements into clear technical plans.

Requirements

• Experience with federated learning, privacy-preserving ML, distributed training, or other multi-party training environments (bonus).
• Experience with Go or other systems programming languages (bonus).
• Experience on production-grade model delivery in regulated, enterprise, pharmaceutical, biotech, or other high-trust environments (bonus).
• Publication record in top-tier ML, computational biology, or structural biology venues such as NeurIPS, ICML, ICLR, ISMB, RECOMB, or similar (bonus).

Benefits

• Industry-competitive compensation, including early-stage virtual share options.
• Remote-first working – work 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 HQ or a different European location (3x per year).
• A high-calibre, execution-focused team with experience from leading organizations.