Machine Learning Intern, Insitro

• Working towards a BS, MS, or Ph.D. in engineering, computational biology, systems biology, computer science, mathematics, statistics, life science, chemistry, physics, or a related field
• Proficiency in one or more general-purpose programming languages. We primarily use Python
• Interest in using and developing brand new statistical and machine learning methods inspired by real problems
• Curiosity about human physiology or disease biology
• Committed to writing high-quality, well-commented code and documentation
• Ability to communicate effectively and collaborate with people of diverse backgrounds and job functions
• Passion for making a difference in the world

Desirable

• We are looking for highly motivated interns to join our compute team as a machine learning scientist looking to work at the intersection of machine learning and life sciences for our Summer 2026 cohort
• You will partner directly with a team mentor in developing and/or applying ML methods to a process and analyze large scale datasets from multiple modalities over the course of the summer (11-12 weeks)
• Compute is a diverse team that works across the company spanning imaging, omics, statistical genetics, pan-modality therapeutics discovery, clinical research, and research software engineering
• Leverage publicly available single cell transcriptomics resources to extract insights about disease mechanisms relevant to the therapeutic areas
• Develop, productionize, and deploy cutting-edge ML approaches to integrate large-scale multi-modal phenotypic datasets
• Develop workflows to enable post-GWAS (Genome-Wide Association Scan) analysis of results, e.g., fine-mapping
• Translational genetics deep dives: enabling higher throughput annotation and exploration of candidate genes from our discovery efforts
• Design of statistical methods to improve rare variant burden tests, and methods to improve power in longitudinal phenotypes
• Develop ML models for imputing disease-relevant phenotypes from high-content clinical imaging datasets, e.g., MRI, PET-CT
• Develop ML methods for disentangling and genetically interpreting axes of variation in complex phenotypes
• Use LLMs to extract disease-relevant information from medical records
• Explore generative models of small molecules, biologics, and/or oligonucleotide therapeutics in various data modalities such as 2D and 3D representations for hit-to-lead drug discovery efforts
• Develop new geometric deep learning methods to better characterize nuanced molecular properties and relationships
• Identify and prototype novel microscopy-driven phenotyping workflows, including hardware acquisition, post-processing, and featurization
• Develop robust software tooling to support the deployment of new and existing methods for general use by insitro scientists
• Optimize existing microscopy acquisition methods in both hardware and software, using ML feature outputs to benchmark improvements
• What you will learn through this experience:
• In the course of the internship you will learn diverse machine learning techniques and rigorously analyze complex dataset and design metrics to ensure robustness of our methods
• You can expect to develop and prototype solutions to enable ML based decisions in our workflows
• You will work closely with machine learning engineers and scientists, biologists, chemists, microscopy experts, and automation engineers
• You will be mentored by one of our senior researchers, who has significant experience in machine learning and/or data science
• You will also attend our machine learning team meetings and be exposed to a diverse set of novel technologies and machine learning concepts that tackle various biological questions
• In return, we will support you by:
• Placing a high degree of trust in your ideas and execution
• Bringing you up to speed in the domain ML enabled drug discovery
• Striving to provide a low-stress work environment
• Making ourselves available for collaboration
• Caring about you as a whole person - not a resource
• Being a well funded startup with a stable runway