Data selection and quality evaluation for biological foundation models

Highlights

• Design large-scale experiments that generate training data for foundation models.
• Pioneer next-gen AI-designed drugs with potential to impact billions.
• Improve assay design and turn biological insights into scalable data strategies.

What they offer

• 30 days paid vacation per year
• Comprehensive health insurance for US based employees
• 401K with company match for US and Direktversicherung for German employees
• Quarterly company-wide retreats
• Monthly wellness benefit
• Budget for visits to offices in Berlin, Palo Alto or Switzerland
• Learning & Development budget for conferences and courses

Ideal candidate

Quantitative biologist who thrives where experiments meet machine learning, curious to learn beyond their domain and motivated to turn messy biology into high-quality data for AI drug design.

Requirements

• PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics or related field or equivalent experience
• Track record analyzing complex biological datasets and translating insights into experiments
• Strong foundation in experimental design, statistical analysis and quantitative reasoning
• Deep understanding of experimental variability, batch effects and assay artifacts
• Capable programmer in Python and common scientific computing libraries
• Excellent written and verbal communication across computational and experimental teams
• Availability to work across US and Europe, meetings 8am PT to 7pm CET
• Readiness to travel several times a year for retreats and business events