Apheris
Technical Lead - Structural Biology Networks(m/w/x)
Full-timeWith HomeofficeSenior
Berlin
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APApheris
Director of ML Research – AI Applications(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Designing and enhancing foundation models for structural biology and co-folding in federated data networks. 7+ years relevant experience and 3+ years technical leadership required. Early-stage virtual share options, wellbeing budget.
Requirements
- Postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or related field
- 7+ years relevant experience
- 3+ years technical leadership experience
- Strong experience applying ML to biological problems
- Experience in structural biology or ADMET
- Proven publication record in top-tier ML or computational biology venues
- Hands-on experience with modern ML systems
- Experience with large-scale models
- Comfortable operating as player-coach
- Setting technical direction
- Leading teams
- Direct contribution to modelling and experimentation
- Effective in cross-functional environments
- Effective in customer-facing environments
- Translate ambiguous scientific problems into technical approaches
- Experience in early-stage biotech
- Experience building ML systems from scratch
- Experience building research functions from scratch
- Experience training large models
- Experience with distributed training across GPU clusters or cloud platforms
- Strong ML Ops experience
- Strong machine learning infrastructure experience
- Experience with Kubernetes-based workflows
- Experience developing QSAR models
- Experience writing Triton kernels
- Experience optimising model performance at systems level
- Experience in federated learning
- Experience in privacy-preserving ML
- Experience in multi-party training environments
Tasks
- Set up and lead ML Research team
- Work alongside existing engineering teams
- Establish research mandate for organization
- Design and enhance foundation models
- Train models for structural biology and co-folding
- Address core challenges in protein interaction modelling
- Leverage large-scale datasets for improved data pipelines
- Develop model architectures capturing geometric and physical priors
- Translate ML advances into practical drug discovery solutions
- Lead cross-functional delivery across teams
- Ensure research integrates into production workflows
- Collaborate with academic partners on research
- Contribute to publications and conference presentations
- Represent Apheris in customer discussions
- Solve high-impact modelling problems for pharma partners
- Build and mentor a high-performing team
- Define initial research roadmap
- Begin hands-on work on co-folding model generalisation
- Deliver initial results and customer-ready analyses
- Establish strong collaboration patterns
- Clarify team capability and hiring plan
- Own portfolio of applied research workstreams
- Be recognized as technical authority in discussions and collaborations
Work Experience
- 7 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenFold
- Boltz
- AWS
- Azure
- Kubernetes
- Triton
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
Team Events
- Office days in Berlin HQ or European location (3x per year)
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
Director of ML Research – AI Applications(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Designing and enhancing foundation models for structural biology and co-folding in federated data networks. 7+ years relevant experience and 3+ years technical leadership required. Early-stage virtual share options, wellbeing budget.
Requirements
- Postgraduate degree (PhD or MSc) in Computer Science, Machine Learning, Computational Biology, or related field
- 7+ years relevant experience
- 3+ years technical leadership experience
- Strong experience applying ML to biological problems
- Experience in structural biology or ADMET
- Proven publication record in top-tier ML or computational biology venues
- Hands-on experience with modern ML systems
- Experience with large-scale models
- Comfortable operating as player-coach
- Setting technical direction
- Leading teams
- Direct contribution to modelling and experimentation
- Effective in cross-functional environments
- Effective in customer-facing environments
- Translate ambiguous scientific problems into technical approaches
- Experience in early-stage biotech
- Experience building ML systems from scratch
- Experience building research functions from scratch
- Experience training large models
- Experience with distributed training across GPU clusters or cloud platforms
- Strong ML Ops experience
- Strong machine learning infrastructure experience
- Experience with Kubernetes-based workflows
- Experience developing QSAR models
- Experience writing Triton kernels
- Experience optimising model performance at systems level
- Experience in federated learning
- Experience in privacy-preserving ML
- Experience in multi-party training environments
Tasks
- Set up and lead ML Research team
- Work alongside existing engineering teams
- Establish research mandate for organization
- Design and enhance foundation models
- Train models for structural biology and co-folding
- Address core challenges in protein interaction modelling
- Leverage large-scale datasets for improved data pipelines
- Develop model architectures capturing geometric and physical priors
- Translate ML advances into practical drug discovery solutions
- Lead cross-functional delivery across teams
- Ensure research integrates into production workflows
- Collaborate with academic partners on research
- Contribute to publications and conference presentations
- Represent Apheris in customer discussions
- Solve high-impact modelling problems for pharma partners
- Build and mentor a high-performing team
- Define initial research roadmap
- Begin hands-on work on co-folding model generalisation
- Deliver initial results and customer-ready analyses
- Establish strong collaboration patterns
- Clarify team capability and hiring plan
- Own portfolio of applied research workstreams
- Be recognized as technical authority in discussions and collaborations
Work Experience
- 7 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Python
- PyTorch
- OpenFold
- Boltz
- AWS
- Azure
- Kubernetes
- Triton
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
Team Events
- Office days in Berlin HQ or European location (3x per year)
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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