(Senior) ML Scientist

insitro•South San Francisco, CA

1d•Hybrid

About The Position

State-of-the-art technologies that measure multiple cellular aspects of in-vitro biology are at the heart of insitro's efforts to accelerate drug development. Computational biology is key to elucidating the relationship between these phenotypes and human disease and translating them into actionable outcomes. This role seeks an expert in ML method development for biological data analysis, covering domains such as network analysis, systems biology, graph-based modeling, causal structure learning, single cell omics, or imaging modalities. The ML Scientist will help the team navigate the complexities of developing disease relevant cell models and analyzing high throughput phenotypic screens, ensuring tools are calibrated and effective, and analyses adhere to the highest rigor and best practices. The position involves close collaboration with experimental biologists, computational biologists, and other machine learning scientists to identify novel phenotypes, develop new screening paradigms, and advance disease understanding. The individual will develop and utilize diverse machine learning and bioinformatic methods for downstream analyses, including integrating with other data modalities like human cohort data to extract insights about disease mechanisms. This is a cross-functional role within a team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists focused on identifying therapeutic targets and developing drugs with high efficacy and low toxicity. The role reports to the Head of Computational Biology and ML-Omics and is a hybrid position requiring at least three days per week in the South San Francisco headquarters. Joining insitro, a vibrant biotech startup, offers opportunities for significant impact, working with a talented team, learning a broad range of skills, and helping shape the company's culture, strategic direction, and outcomes.

Requirements

Ph.D. in computer science, machine learning, computational biology, systems biology, or a related discipline
Extensive hands on experience developing ML methods for biological data modalities
Hands on experience with biological data analysis, in particular familiarity with network and graph based analysis and modeling techniques
Experience integrating data or insights from multiple sources and distinct modalities (e.g., imaging, transcriptomics, functional genomics, genetics, human cohort data)
Strong programming skills in scientific programming languages (i.e., Python)
Committed to writing well-commented code and documentation, and familiarity with coding best practices (i.e. version control, code review)
Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions
Publication record of meaningful contributions to high-quality work in relevant machine learning, computational biology, systems biology, life sciences, or biomedical venues
Passion for developing useful and impactful methods and making a difference in the world

Nice To Haves

Experience with statistical genetics and integrating functional and omics data with gwas
First-hand experience studying disease biology
Passionate about problem solving, asking questions and learning independently
Experience with gene regulatory network inference or causal modeling
Familiarity with cloud computing services (e.g., AWS or azure)
Demonstrated ability to write software in a team, industry experience or substantial involvement with open source projects
Experience building infrastructure for data processing

Responsibilities

Develop ML methods for biological data analysis, in domains such as network analysis, systems biology, graph-based modeling, causal structure learning, single cell omics, or imaging modalities
Help the team navigate the complexities of developing disease relevant cell models and analyzing high throughput phenotypic screens
Ensure that the tools being developed are calibrated and effective, and that analyses are performed to the highest rigor and in line with best practices in the broader scientific community
Collaborate closely with experimental biologists, computational biologists, and other machine learning scientists
Support the identification of novel phenotypes
Support the development of new screening paradigms
Advance our understanding of disease
Develop and utilize diverse machine learning and bioinformatic methods to perform diverse downstream analyses, including integrating with other data modalities, including human cohort data in order to extract insights about disease mechanisms
Be part of a cross-functional team of life scientists, data scientists, bioengineers, software engineers, and machine learning scientists that strive to identify therapeutic targets and develop drugs of high efficacy and low toxicity
Help shape insitro’s culture, strategic direction, and outcomes

Benefits

401(k) plan with employer matching for contributions
Excellent medical, dental, and vision coverage as well as mental health and well-being support
Open, flexible vacation policy
Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)
Quarterly budget for books and online courses for self-development
Support to attend professional conferences that are meaningful to your career growth and role's responsibilities
New hire stipend for home office setup
Monthly cell phone & internet stipend
Access to free onsite baristas and daily lunch for employees who are either onsite or hybrid
Access to a free commuter bus network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area