Building the Experimental Engine for Models of Life
Inceptive
| Company | Inceptive |
| Category | Uncategorised |
| Location | Palo Alto |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Not stated |
| Salary | Not stated by the employer |
| Posted | 13 Aug 2026 |
| Last verified | 14 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
Inceptive builds foundation models of life to enable medicines otherwise out of reach. We are seeking an exceptional scientific leader to build the experimental engine that trains, tests, and improves models that design medicines.
You will integrate molecular and cellular biology, functional genomics, screening, automation, and therapeutic development into a coherent experimental platform. Working closely with ML and translational biology experts, you will design and pioneer experiments to most effectively expand our models’ understanding of life and ability to design better medicines. This is a role for a scientific builder: someone who can identify transformative experimental opportunities, rapidly onboard and, where necessary, invent the required technologies, and assemble outstanding teams to turn ambitious ideas into scalable systems.
Responsibilities
Define experimental strategy to close gaps in model understanding and therapeutic design performance
Design experiments specifically to train and evaluate generative and predictive models, in close collaboration with biologists and ML experts
Generate high-quality, model-ready data using scalable methods such as functional genomics, high-throughput screening, multi-omics, and phenotypic profiling
Integrate assay development, automation, data generation, analysis, and interpretation in a fast-paced, collaborative environment
Recruit and lead a multidisciplinary team of experimental scientists and technology builders
Qualifications
An outstanding record of innovation in highly scalable biological assays that advance the state-of-the-art in therapeutic discovery, molecular design, or functional genomics
Deep understanding of how experimental design, measurement quality, and biological context determine what models can learn from data
Excellent communication across discipline boundaries, including fluency in analyzing and visualizing complex biological data
Availability to work with team members across US and Europe, with meetings starting at 7am PT
Readiness to travel several times a year for company retreats and business events
We value in-person collaboration and expect candidates to work at our lab location
Preferred experience
Leadership of a research team of 10+ scientists
Hands-on experience in mammalian cell engineering, RNA biology, and biochemistry
Hands-on experience using laboratory automation to scale up data generation
Development of data-rich in vivo assays, eg, incorporating pooled libraries, single-cell analysis, or spatial profiling
Compensation
$245K – $305K + Bonus + Equity
What we offer
A competitive compensation package
30 days paid vacation per year
Comprehensive health insurance for US based Beginners
401K with company match for US based Beginners and Direktversicherung for German Beginners
Quarterly company-wide retreats
Monthly wellness benefit
Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland
Learning & Development budget to attend conferences, take courses, or otherwise invest in your professional growth, as well as access to the Learning & Development platform EdX and Hone
A buddy to help you get settled
*Varies by country and does not apply to internships
At Inceptive, we are creating tools to develop increasingly powerful biological software for the rational design of novel, broadly accessible medicines and biotechnologies previously out of reach. Our team brings together vast expertise in molecular biology, machine learning, and software engineering, and we are all working towards becoming antedisciplinary, meaning we deepen the knowledge we have in our area of expertise while also expanding our knowledge of completely new fields.
We approach our goals with a Beginner's mind, humbly and with fresh eyes, and aim to become the pioneers of a new discipline rooted in biology as much as in deep learning