Apheris
Technical Lead - Structural Biology Networks(m/w/x)
Full-timeWith HomeofficeSenior
Berlin
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APApheris
Technical Lead – Large Molecule AI Systems(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Building federated large molecule AI systems for drug discovery, hands-on with antibody modeling and binder prediction. 5+ years applying ML to biological problems required. Early-stage virtual share options, wellbeing budget.
Requirements
- PhD, MSc, or equivalent experience in relevant field
- 5+ years applying ML to scientific/biological problems
- Experience in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design
- Hands-on experience with modern ML systems in Python and PyTorch
- Worked with or extended large-scale models (OpenFold, AlphaFold, Boltz, ESM, or similar)
- MLOps or ML infrastructure experience
- Kubernetes-based training, evaluation, or deployment workflows
- Define success criteria and validate model quality
- Ensure ML releases are robust for real-world use
- Led delivery of complex ML projects
- Set technical direction for ML projects
- Managed risks and dependencies in ML projects
- Drove teams toward high-quality ML releases
- Player-coach role: mentoring engineers and ML scientists
- Contribute directly to modeling, experimentation, or architecture
- Work effectively with product, research, leadership, customers, and scientific stakeholders
- Turn ambiguous requirements into clear technical plans
- Experience with federated learning, privacy-preserving ML, or distributed training
- Experience in multi-party training environments
- Production-grade model delivery in regulated/enterprise environments
- Production-grade model delivery in pharmaceutical/biotech environments
- Production-grade model delivery in high-trust environments
- Publication record in top-tier ML venues
- Publication record in computational biology venues
- Publication record in structural biology venues
Tasks
- Lead teams in building and delivering federated large molecule AI systems
- Stay hands-on with antibody modeling, co-folding, binder prediction, and developability
- Build and implement ML applications for large biomolecular foundation models
- Ensure high-quality model releases ship on time against committed milestones
- Translate ambiguous scientific and technical goals into clear plans and priorities
- Guide evaluation decisions and deliver results packages to external stakeholders
- Surface risks, blockers, bugs, timeline changes, and technical trade-offs early
- Align consortium members on objectives, evaluation criteria, data requirements, and delivery expectations
- Work with product, engineering, research, and leadership to shape the model roadmap
Work Experience
- 5 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenFold
- AlphaFold
- Boltz
- ESM
- Kubernetes
- federated learning
- privacy-preserving ML
- distributed training
Benefits
Competitive Pay
- Industry-competitive compensation
- Early-stage virtual share options
Additional Allowances
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mental Health Support
- Mental health support
Learning & Development
- Learning budget
More Vacation Days
- Generous holiday allowance
Other Benefits
- Office days at Berlin HQ or European location
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Apheris and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
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APApheris
Technical Lead – Large Molecule AI Systems(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Building federated large molecule AI systems for drug discovery, hands-on with antibody modeling and binder prediction. 5+ years applying ML to biological problems required. Early-stage virtual share options, wellbeing budget.
Requirements
- PhD, MSc, or equivalent experience in relevant field
- 5+ years applying ML to scientific/biological problems
- Experience in structural biology, antibody engineering, biologics discovery, developability prediction, binder prediction, or protein design
- Hands-on experience with modern ML systems in Python and PyTorch
- Worked with or extended large-scale models (OpenFold, AlphaFold, Boltz, ESM, or similar)
- MLOps or ML infrastructure experience
- Kubernetes-based training, evaluation, or deployment workflows
- Define success criteria and validate model quality
- Ensure ML releases are robust for real-world use
- Led delivery of complex ML projects
- Set technical direction for ML projects
- Managed risks and dependencies in ML projects
- Drove teams toward high-quality ML releases
- Player-coach role: mentoring engineers and ML scientists
- Contribute directly to modeling, experimentation, or architecture
- Work effectively with product, research, leadership, customers, and scientific stakeholders
- Turn ambiguous requirements into clear technical plans
- Experience with federated learning, privacy-preserving ML, or distributed training
- Experience in multi-party training environments
- Production-grade model delivery in regulated/enterprise environments
- Production-grade model delivery in pharmaceutical/biotech environments
- Production-grade model delivery in high-trust environments
- Publication record in top-tier ML venues
- Publication record in computational biology venues
- Publication record in structural biology venues
Tasks
- Lead teams in building and delivering federated large molecule AI systems
- Stay hands-on with antibody modeling, co-folding, binder prediction, and developability
- Build and implement ML applications for large biomolecular foundation models
- Ensure high-quality model releases ship on time against committed milestones
- Translate ambiguous scientific and technical goals into clear plans and priorities
- Guide evaluation decisions and deliver results packages to external stakeholders
- Surface risks, blockers, bugs, timeline changes, and technical trade-offs early
- Align consortium members on objectives, evaluation criteria, data requirements, and delivery expectations
- Work with product, engineering, research, and leadership to shape the model roadmap
Work Experience
- 5 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenFold
- AlphaFold
- Boltz
- ESM
- Kubernetes
- federated learning
- privacy-preserving ML
- distributed training
Benefits
Competitive Pay
- Industry-competitive compensation
- Early-stage virtual share options
Additional Allowances
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mental Health Support
- Mental health support
Learning & Development
- Learning budget
More Vacation Days
- Generous holiday allowance
Other Benefits
- Office days at Berlin HQ or European location
Find the original job posting in its most current version here. Nejo automatically captured this job from the website of Apheris and processed the information on Nejo with the help of AI for you. Despite careful analysis, some information may be incomplete or inaccurate. Please always verify all details in the original posting! Content and copyrights of the original posting belong to the advertising company.
Like this job?
BetaYour Career Agent finds similar jobs for you every day.
About the Company
Apheris
Industry
Pharmaceuticals
Description
Apheris builds AI applications for pharmaceutical R&D, enabling secure collaboration on large AI models to accelerate drug discovery.
Not a perfect match?
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