Data selection and quality evaluation for biological foundation models

Inceptive is building the next generation of AI‑designed medicines by training large‑scale biological foundation models. The company combines molecular biology, machine learning and software engineering to create tools that can design novel drugs and biotechnologies. This role, Data selection and quality evaluation for biological foundation models, sits at the intersection of data science and experimental biology, helping to generate the high‑quality experimental data that powers those models. Day‑to‑day you will work hand‑in‑hand with biologists and machine‑learning researchers to shape the experiments that feed the models. Your tasks include designing and analysing large‑scale biological assays, developing statistical and computational methods to assess assay quality, reproducibility and sources of variation, and spotting bias or measurement artifacts in datasets. You will partner with experimental scientists to improve assay design, controls and data‑collection strategies, and collaborate with ML teams to understand how experimental decisions affect model training and evaluation. Findings will be visualised and communicated clearly to support decision‑making across scientific and engineering groups. The position requires a PhD (or equivalent practical experience) in a quantitative life‑science discipline such as computational biology, systems biology, genomics, bioengineering, biostatistics or biophysics. Candidates must have a proven record of analysing complex biological data and turning computational insights into experimental validation or new data collection. A solid grounding in experimental design, statistical analysis and quantitative reasoning is essential, as is deep knowledge of batch effects, assay artifacts and other sources of experimental variability. Strong Python programming skills with common scientific libraries, excellent written and verbal communication, and the ability to work across US and European time zones (meetings from 8 am PT to 7 pm CET) are also required. The role expects regular travel for company retreats and business events and a preference for office‑based collaboration at Inceptive's locations. Preferred, but not mandatory, experience includes more than three years of post‑PhD work in computational biology or a related field and a background linking experimental outcomes to machine‑learning model development and evaluation. The team operates in an "antedisciplinary" environment that encourages learning beyond one's primary expertise. Employees enjoy a competitive salary range of $135 K, $240 K plus bonus and equity, 30 days of paid vacation, comprehensive health coverage, retirement plans, quarterly retreats, a monthly wellness allowance, and a budget for travel to Berlin, Palo Alto or Switzerland offices as well as professional development. What the role asks for: - PhD in computational/quantitative biology or equivalent experience - Proven analysis of complex biological datasets with experimental validation - Strong expertise in experimental design and statistical analysis - Deep knowledge of batch effects and assay artifacts - Proficient Python programming with scientific libraries - Excellent written and verbal communication across disciplines - Availability for US‑Europe meetings (8 am PT, 7 pm CET) - Willingness to travel several times per year - 3+ years post‑PhD experience in computational biology (nice‑to‑have) - Experience linking experimental results to machine‑learning model development (nice‑to‑have)