Data selection and quality evaluation for biological foundation models

Inceptive; Palo Alto (CA); On-site; USD 140,000 - 220,000; Full time

Job summary

Inceptive in Palo Alto is seeking a PhD-level computational biology expert to help pioneer AI-driven drug design. You will design and analyze large-scale biological experiments that power machine learning models, and you will collaborate with biologists and ML researchers across US and Europe.

You will translate experimental insights into scalable data-generation strategies, assess assay quality, and communicate findings to engineering and scientific teams.

Qualifications

• 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 location.

Responsibilities

• 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 data.
• 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.

Skills

Python

Statistical analysis

Experimental design

Data analysis

Biology knowledge

Education

PhD in computational biology

Tools

R

NumPy/SciPy

Job description

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 strategie

s.
Your Mission, should you choose to accept

• itEmbody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expert
• iseDevelop statistical and computational approaches to characterize assay quality, reproducibility, and sources of experimental variat
• ionIdentify and investigate sources of bias and measurement artifacts in biological datas
• etsDesign and analyze large-scale biological experiments that generate training and evaluation data for machine learning mod
• elsPartner with experimental scientists to improve assay design, controls, and data collection strateg
• iesCollaborate with machine learning researchers to understand how experimental design decisions impact model training and evaluat
• ionAnalyze, visualize, and communicate findings to support decision-making across scientific and engineering te

ams
Qualificat

• ionsPhD in computational biology, systems biology, genomics, bioengineering, biostatistics, biophysics, or a related quantitative discipline, or equivalent practical experi
• enceDemonstrated track record of analyzing complex biological datasets and translating computational insights into experimental validation or new data collec
• tionStrong foundation in experimental design, statistical analysis, and quantitative reaso
• ningDeep understanding of sources of experimental variability, batch effects, and assay artifacts in biological
• dataCapable programmer in Python and common scientific computing libra
• riesExcellent written and verbal communication skills, including the ability to communicate effectively across computational and experimental discipl
• inesAvailability to work with team members across US and Europe, with meetings starting at 8am PT and ending at 7pm
• CETReadiness to travel several times a year for company retreats and business ev
• entsWe value the benefits of in-person collaboration and expect candidates to primarily work from our office locat

ions
Preferred technical s

• kills3+ years of post-PhD experience in computational biology, biostatistics, or a related
• fieldExperience connecting experimental outcomes to machine learning model development and evalu