Apheris
Senior Director, Computational Drug Discovery(m/w/x)
Full-timeWith HomeofficeSenior
Berlin
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APApheris
Senior ML Researcher – Molecular Privacy(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Designing privacy risk experiments for molecular and structural ML pipelines in pharmaceutical R&D. Proficiency in structure-based protein-ligand modelling and ML privacy experience required. Early-stage virtual share options, wellbeing budget, work-from-home budget.
Requirements
- Experience with drug discovery ML models
- Proficiency in structure-based protein-ligand modelling
- Practical reasoning about privacy risk
- Experience building structure-based modelling and co-folding
- Experience with ML privacy or federated learning
- Ability to design empirical privacy experiments
- Clear communication of complex technical risks
- Ownership of ambiguous cross-cutting problems
- Published technical work in ML or biology
- Experience in multi-organization industry collaborations
- Experience defining privacy or risk positions
- Experience acting as a technical authority
Tasks
- Design and execute practical privacy risk experiments
- Map theoretical threats to realistic attack surfaces
- Analyze molecular and structural machine learning pipelines
- Identify sensitive signal exposure in modeling choices
- Build experimental tooling for privacy analysis
- Conduct generative reconstruction and distributional leakage tests
- Generate technically credible privacy evidence through modeling
- Create informative reports for consortium decision-makers
- Translate empirical findings into clear privacy narratives
- Collaborate on grounded privacy mitigation strategies
- Reproduce modeling pipelines for baseline attack assessments
- Analyze active federated drug discovery programs
- Run privacy experiments on live federated workflows
- Synthesize quantitative evidence into stakeholder presentations
- Act as a technical authority on privacy
- Evaluate privacy risks under real operational conditions
- Shape organizational standards for privacy experimentation
Work Experience
- approx. 4 - 6 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Machine learning models
- Structure-based modelling
- Protein–ligand modelling
- ADMET
- Co-folding
- Federated learning
- Computational biology
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 benefits
Learning & Development
- Learning and development budget
Free or Subsidized Food
- Regular team lunches
Team Events
- Social events
- Quarterly All Hands meet-ups
More Vacation Days
- Generous holiday allowance
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
Senior ML Researcher – Molecular Privacy(m/w/x)
Berlin
Full-timeWith Home OfficeSenior
AI/ML
Data Science
Nejo AI Summary
Designing privacy risk experiments for molecular and structural ML pipelines in pharmaceutical R&D. Proficiency in structure-based protein-ligand modelling and ML privacy experience required. Early-stage virtual share options, wellbeing budget, work-from-home budget.
Requirements
- Experience with drug discovery ML models
- Proficiency in structure-based protein-ligand modelling
- Practical reasoning about privacy risk
- Experience building structure-based modelling and co-folding
- Experience with ML privacy or federated learning
- Ability to design empirical privacy experiments
- Clear communication of complex technical risks
- Ownership of ambiguous cross-cutting problems
- Published technical work in ML or biology
- Experience in multi-organization industry collaborations
- Experience defining privacy or risk positions
- Experience acting as a technical authority
Tasks
- Design and execute practical privacy risk experiments
- Map theoretical threats to realistic attack surfaces
- Analyze molecular and structural machine learning pipelines
- Identify sensitive signal exposure in modeling choices
- Build experimental tooling for privacy analysis
- Conduct generative reconstruction and distributional leakage tests
- Generate technically credible privacy evidence through modeling
- Create informative reports for consortium decision-makers
- Translate empirical findings into clear privacy narratives
- Collaborate on grounded privacy mitigation strategies
- Reproduce modeling pipelines for baseline attack assessments
- Analyze active federated drug discovery programs
- Run privacy experiments on live federated workflows
- Synthesize quantitative evidence into stakeholder presentations
- Act as a technical authority on privacy
- Evaluate privacy risks under real operational conditions
- Shape organizational standards for privacy experimentation
Work Experience
- approx. 4 - 6 years
Education
- Master's degree
Languages
- English – Business Fluent
Tools & Technologies
- Machine learning models
- Structure-based modelling
- Protein–ligand modelling
- ADMET
- Co-folding
- Federated learning
- Computational biology
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 benefits
Learning & Development
- Learning and development budget
Free or Subsidized Food
- Regular team lunches
Team Events
- Social events
- Quarterly All Hands meet-ups
More Vacation Days
- Generous holiday allowance
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.
About the Company
Apheris
Industry
Science
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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