Data Generation for ML-based molecule design at Inceptive

Palo Alto, California, United States -

Full Time

Start Date

Immediate

Expiry Date

15 Oct, 26

Salary

240000.0

Posted On

17 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Statistical Analysis, Experimental Design, Computational Biology, Quantitative Reasoning, Data Visualization, Biostatistics, Genomics, Bioengineering, Biophysics, Machine Learning, Data Analysis

Industry

Biotechnology Research

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 strategies. 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 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 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 locations Preferred technical skills 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 Compensation $135K – $240K + 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, whose impact will be realized together with out-of-the-box thinkers in business and entrepreneurship, defying established categorizations. We are building a company culture centered around growth, learning, and discovery. We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for. It is the policy of Inceptive to ensure equal employment opportunity without discrimination or harassment on the basis of race, color, religion, sex, sexual orientation, gender identity or expression, age, disability, marital status, citizenship, national origin, genetic information, or any other characteristic protected by law. Inceptive prohibits any such discrimination or harassment. Inceptive is also committed to welcoming and providing accommodations to people with disabilities. Please let us know if you need any accommodations throughout your application process.

Responsibilities

Design and analyze large-scale biological experiments to generate high-quality training data for ML-based molecule design. Collaborate with biologists and ML researchers to identify measurement artifacts and translate biological insights into scalable data strategies.