Computational design of biological experiments for model development

At Inceptive, you will help pioneer the next generation of AI-designed drugs, with the potential to positively impact billions of people, as part of a collaborative, antedisciplinary team.

We advance the state of the art in molecular design by training large-scale foundation models that enable cutting-edge generative approaches. Those models learn from diverse biological datasets and are refined through focused experimentation, large-scale training, and feedback from lab measurements. Progress depends not only on building better models, but also on understanding what they learn, where they fail, how data shapes their behavior, and how to evaluate them against biologically meaningful objectives.

You will collaborate closely with AI researchers and biologists to rigorously characterize the behavior, capabilities, and limitations of biological foundation models and their applications. You will identify valuable datasets, develop meaningful evaluations, investigate model behavior, and generate insights that guide model development, data strategy, and experimental priorities across the company.

Your Mission, should you choose to accept it

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Investigate how model performance changes with data quantity, data quality, dataset composition, and training methodology
• Develop biologically meaningful evaluations and benchmarks that measure progress toward therapeutic design objectives
• Design and execute rigorous experiments to understand the behavior, capabilities, and limitations of biological foundation models
• Identify sources of potential artifacts, bias, and noise in biological datasets
• Identify promising biological datasets for model training and evaluation, and develop computational pipelines for preprocessing, quality control, and exploratory analysis.
• Design studies, in silico or in the lab, that reveal what models have learned and which biological signals drive model behavior
• Work with biologists to formulate hypotheses and translate biological questions into measurable machine learning experiments
• Partner with AI researchers and engineers to prioritize research directions, data collection efforts, and model improvements
• Analyze, visualize, and communicate experimental findings to inform decisions across teams

Qualifications

• PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience, with record of publications or open source tooling in these fields
• Strong quantitative reasoning and statistical intuition
• Demonstrated ability to identify important scientific questions, design rigorous investigations, and draw reliable conclusions from complex biological data.
• Experience analyzing high throughput sequencing data (e.g. RNA-seq, functional genomics / transcriptomics, MPRA), with a focus on robust statistical analysis
• Experience collaborating closely with AI/machine learning researchers or applying machine learning or generative AI tools to scientific problems
• Familiarity with current AI/machine learning methods, including generative foundation models, representation learning, and model evaluation
• Familiarity with publicly available biological datasets and data derived from high throughput assays
• Capable programmer in Python and common scientific computing libraries
• Excellent written and verbal communication skills, including the ability to explain complex findings to audiences with diverse technical backgrounds
• Availability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm CET
• Readiness to travel several times a year for company retreats and business events
• We value the benefits of in-person collaboration and expect candidates to primarily work from our office locations

Preferred technical skills

• 3+ years of post-PhD experience in computational biology, biostatistics, machine learning research, or a related field