Apheris
Senior Director, Computational Drug Discovery(m/w/x)
Vollzeitmit HomeofficeSenior
Berlin
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APApheris
Senior ML Researcher – Molecular Privacy(m/w/x)
Berlin
Vollzeitmit HomeofficeSenior
AI/ML
Data Science
Nejo KI-Zusammenfassung
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.
Anforderungen
- 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
Aufgaben
- 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
Berufserfahrung
- ca. 4 - 6 Jahre
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Machine learning models
- Structure-based modelling
- Protein–ligand modelling
- ADMET
- Co-folding
- Federated learning
- Computational biology
Benefits
Attraktive Vergütung
- Industry-competitive compensation
- Early-stage virtual share options
Sonstige Zulagen
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mentale Gesundheitsförderung
- Mental health benefits
Weiterbildungsangebote
- Learning and development budget
Gratis oder Vergünstigte Mahlzeiten
- Regular team lunches
Team Events & Ausflüge
- Social events
- Quarterly All Hands meet-ups
Mehr Urlaubstage
- Generous holiday allowance
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens Apheris erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Noch nicht perfekt?
- Clue
(Senior) ML Engineer(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Apheris
Senior Product Designer(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Intercom
Senior Machine Learning Scientist(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Fresenius Medical Care
Senior Expert – Technical Privacy(m/w/x)
VollzeitRemoteSeniorBerlin
APApheris
Senior ML Researcher – Molecular Privacy(m/w/x)
Berlin
Vollzeitmit HomeofficeSenior
AI/ML
Data Science
Nejo KI-Zusammenfassung
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.
Anforderungen
- 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
Aufgaben
- 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
Berufserfahrung
- ca. 4 - 6 Jahre
Ausbildung
- Master-Abschluss
Sprachen
- Englisch – verhandlungssicher
Tools & Technologien
- Machine learning models
- Structure-based modelling
- Protein–ligand modelling
- ADMET
- Co-folding
- Federated learning
- Computational biology
Benefits
Attraktive Vergütung
- Industry-competitive compensation
- Early-stage virtual share options
Sonstige Zulagen
- Wellbeing budget
- Work-from-home budget
- Co-working stipend
Mentale Gesundheitsförderung
- Mental health benefits
Weiterbildungsangebote
- Learning and development budget
Gratis oder Vergünstigte Mahlzeiten
- Regular team lunches
Team Events & Ausflüge
- Social events
- Quarterly All Hands meet-ups
Mehr Urlaubstage
- Generous holiday allowance
Die Originalanzeige dieses Stellenangebotes in der aktuellsten Version findest du hier. Nejo hat diesen Job automatisch von der Website des Unternehmens Apheris erfasst und die Informationen auf Nejo mit Hilfe von KI für dich aufbereitet. Trotz sorgfältiger Analyse können einzelne Informationen unvollständig oder ungenau sein. Bitte prüfe immer alle Angaben in der Originalanzeige! Inhalte und Urheberrechte der Originalanzeige liegen beim ausschreibenden Unternehmen.
Über das Unternehmen
Apheris
Branche
Science
Beschreibung
Apheris builds AI applications for pharmaceutical R&D, enabling secure collaboration on large AI models to accelerate drug discovery.
Noch nicht perfekt?
- Apheris
Senior Director, Computational Drug Discovery(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Clue
(Senior) ML Engineer(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Apheris
Senior Product Designer(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Intercom
Senior Machine Learning Scientist(m/w/x)
Vollzeitmit HomeofficeSeniorBerlin - Fresenius Medical Care
Senior Expert – Technical Privacy(m/w/x)
VollzeitRemoteSeniorBerlin