Data Generation for ML-based molecule design

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 depend on rich, high-quality experimental data that captures biological function. Progress requires not only building better models, but also designing better experiments, understanding measurement systems, and generating datasets that faithfully represent underlying biology.

You will collaborate closely with biologists and machine learning researchers to design, analyze, and improve the experiments that power our models. You will help determine what data should be generated, how experiments should be structured, how measurement artifacts can be identified, and how biological insights can be translated into scalable data generation strategies.

• Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
• Develop statistical and computational approaches to characterize assay quality, reproducibility, and sources of experimental variation
• Identify and investigate sources of bias and measurement artifacts in biological datasets
• Design and analyze large-scale biological experiments that generate training and evaluation data for machine learning models
• Partner with experimental scientists to improve assay design, controls, and data collection strategies
• Collaborate with machine learning researchers to understand how experimental design decisions impact model training and evaluation
• Analyze, visualize, and communicate findings to support decision-making across scientific and engineering teams

• PhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics, or a related quantitative discipline, or equivalent practical experience
• Demonstrated track record of analyzing complex biological datasets and translating computational insights into experimental validation or new data collection
• Strong foundation in experimental design, statistical analysis, and quantitative reasoning
• Deep understanding of sources of experimental variability, batch effects, and assay artifacts in biological data
• Capable programmer in Python and common scientific computing libraries
• Excellent written and verbal communication skills, including the ability to communicate effectively across computational and experimental disciplines
• 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

• 3+ years of post-PhD experience in computational biology, biostatistics, or a related field
• Experience connecting experimental outcomes to machine learning model development and evaluation