(Senior) ML Scientist

About the role

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. We are looking for an expert in ML method development for biological data analysis, in domains such as network analysis, systems biology, graph-based modeling, causal structure learning, single cell omics, or imaging modalities.

Responsibilities

• Collaborate closely with experimental biologists, computational biologists, and other machine learning scientists, support the identification of novel phenotypes, the development of new screening paradigms, and 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.
• 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.

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).
• Able 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.

Qualifications

• Passion for developing useful and impactful methods and making a difference in the world.

Skills

• Nice to have 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.

Benefits

• Annual Performance Bonus Plan (based on company targets by role level and annual company performance)
• Equity Incentive Plan
• 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

Pay

At Insitro, our target starting salary for successful US-based applicants for this role is $183,000 - $238,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.