Inceptive
Characterizing biological foundation models
Palo Alto, CA
Sponsorship not specifiedDetected 5 days ago
PythonMachine LearningDeep LearningLLMsStatisticsA/B TestingBioinformaticsMolecular BiologyResearchCommunicationCollaborationBiostatistics
About the role
- Those models learn from diverse biological datasets and are refined through focused experimentation, large-scale training, and feedback from lab measurements.
- We believe in humility and open-mindedness in how we approach each other, as well as problems we don't yet have solutions for.
- Inceptive prohibits any such discrimination or harassment.
Responsibilities
- 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.
- Develop biologically meaningful evaluations and benchmarks that measure progress toward therapeutic design objectives
- Design and execute rigorous experiments to understand the behavior, capabilities, and limitations of biological foundation models
- Identify promising biological datasets for model training and evaluation, and develop computational pipelines for preprocessing, quality control, and exploratory analysis.
- Design studies, in silico or in the lab, that reveal what models have learned and which biological signals drive model behavior
- Partner with AI researchers and engineers to prioritize research directions, data collection efforts, and model improvements
Requirements
- Experience analyzing high throughput sequencing data (e.g. RNA-seq, functional genomics / transcriptomics, MPRA), with a focus on robust statistical analysis
- Familiarity with publicly available biological datasets and data derived from high throughput assays
- Excellent written and verbal communication skills, including the ability to explain complex findings to audiences with diverse technical backgrounds
- PhD in computational biology, statistics, physics, machine learning, or a related quantitative discipline, or equivalent practical experience, with record of publications or open source tooling in these fields
- Strong quantitative reasoning and statistical intuition
- Demonstrated ability to identify important scientific questions, design rigorous investigations, and draw reliable conclusions from complex biological data.
- Experience collaborating closely with AI/machine learning researchers or applying machine learning or generative AI tools to scientific problems
- Familiarity with current AI/machine learning methods, including generative foundation models, representation learning, and model evaluation
- Capable programmer in Python and common scientific computing libraries
- 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, machine learning research, or a related field
Nice to have
- Preferred technical skills
Compensation
- Budget for multiple visits per year to our offices in Berlin, Palo Alto or Switzerland
Benefits
- 30 days paid vacation per year
- Comprehensive health insurance for US based Beginners
- Monthly wellness benefit
- 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
- We are building a company culture centered around growth, learning, and discovery.
- Embody our vision of an antedisciplinary environment and embrace learning about areas outside of your traditional area of expertise
- Work with biologists to formulate hypotheses and translate biological questions into measurable machine learning experiments
Visa & Work Authorization
- asis 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
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