(Senior) ML Scientist

About the role

Opportunity

Modern biological measurement technologies form the foundation of Insitro's strategy to expedite drug discovery. Computational biology serves as the bridge connecting complex cellular readouts to human disease, transforming raw data into actionable therapeutic strategies.

We seek a computational scientist focused on method creation for biological datasets, analyzing areas such as network modeling, systems biology, graph analytics, causal inference, single-cell analysis, and image analysis. Your work will untangle the challenges of modeling disease-relevant cellular systems and interpreting high-volume phenotypic screens. You will ensure analytical rigor, calibrate biological tools for accuracy, and adhere to community standards.

Collaboration is central to this position. You will work alongside experimental biologists, data scientists, and bioengineers to identify new phenotypes, create screening innovations, and deepen biological understanding. You will develop and apply diverse computational methods for integrated analysis, including the combination of varied data sources such as human cohort information, to reveal disease mechanisms. You will be part of a cross-functional team dedicated to discovering therapeutic targets and developing drugs that are both effective and safe. This position reports to the Head of Computational Biology and ML-Omics and is a hybrid role requiring three days per week at our South San Francisco headquarters.

Joining Insitro means joining a dynamic startup with significant potential for impact. You will collaborate with a brilliant team, rapidly expand your skill set, and help define Insitro's culture, strategy, and results. Join us and contribute to patient outcomes.

About You

• Ph.D. in computer science, machine learning, computational biology, systems biology, or a related field.
• Extensive experience creating machine learning methods tailored for biological data modalities.
• Hands-on history with biological data analysis, especially network and graph-based techniques.
• Demonstrated skill in merging information from multiple sources and data types, such as imaging, transcriptomics, functional genomics, genetics, and cohort studies.
• Strong coding ability in scientific languages, particularly Python.
• Commitment to writing clear code with documentation and adherence to software development best practices like version control and review.
• Effective communication skills and the ability to collaborate with professionals from varied disciplines.
• A record of significant contributions to high-quality publications in relevant machine learning, computational biology, systems biology, life sciences, or biomedical conferences or journals.
• Passion for building practical methods that create real-world impact.

Nice to Have

• Familiarity with statistical genetics and the integration of functional and omics data with genome-wide association studies.
• First-hand knowledge of disease biology.
• Enthusiasm for independent problem-solving and self-directed learning.
• Experience with gene regulatory network inference or causal modeling frameworks.
• Familiarity with cloud services like AWS or Azure.
• A track record of shipping software in team settings, industry background, or significant open-source contributions.
• Experience constructing data processing infrastructure.

Compensation & Benefits at Insitro

Our target starting salary for qualified US-based candidates for this role is $183,000 - $238,000. Base pay is determined by evaluating factors such as your expertise, education, background, market conditions, business requirements, and internal equity. Adjustments may be made based on market trends.

This role qualifies for participation in our Annual Performance Bonus Plan, tied to role-level and company performance goals, and our Equity Incentive Plan, subject to plan terms and policies.

• 401(k) plan with employer matching for contributions
• Comprehensive medical, dental, and vision coverage, including mental health and well well-being support
• Flexible vacation policy
• Paid parental leave of at least 16 weeks for primary caregivers, and 10 weeks for secondary new parents (including birth, adoption, or fostering)
• Quarterly budget for books and online learning
• Support for professional conference attendance

About Insitro

At Insitro, we are building a different kind of drug company to bring better drugs faster to the patients who can benefit most. Through the power of machine learning (ML) and data at scale, we decode the complexities of biology to unlock transformative new medicines.

Key facts

What you'll do

• Architect and implement advanced machine learning models tailored specifically for complex biological and genomic datasets to drive target identification and validation.
• Design and execute experiments that quantify the biological relevance and predictive power of computational methods within realistic in vitro and in vivo contexts.
• Partner with wet laboratory scientists to translate high-dimensional data into testable hypotheses and novel biological assays.
• Develop scalable data processing pipelines capable of integrating multimodal data sources, including imaging, sequencing, and clinical cohort information.
• Lead the application of graph neural networks and network-based algorithms to interpret cellular pathways and disease mechanisms.
• Implement rigorous statistical frameworks and causal inference models to strengthen biological conclusions and reduce confounding effects.
• Create reproducible analysis workflows using modern software engineering practices and version control systems to ensure transparency and scalability.
• Communicate analytical findings and model behaviors to interdisciplinary teams through clear visualizations and structured documentation.
• Mentor junior scientists and collaborators on best practices for machine learning application in biological research.
• Contribute to the strategic planning of the computational biology roadmap and the definition of success metrics for integrated omics programs.