Machine Learning Intern

insitro • South San Francisco, CA

1d • $35 - $65 • Hybrid

About The Position

The Opportunity
Global drug development productivity is declining, with an overall failure to develop effective treatments for many increasingly prevalent complex diseases affecting millions of patients per year. We seek to tackle this by combining innovative machine learning techniques with pioneering technologies that measure multiple cellular aspects, aiming to drastically improve and accelerate how drugs are discovered and developed.
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). These internships can based in on South San Francisco headquarter with a hybrid work schedule or can be remote based on the team mentor's location and business need. 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.
Example of areas & topics you will be working on:
Computational Biology:
Leverage publicly available single cell transcriptomics resources to extract insights about disease mechanisms relevant to the therapeutic areas;
Methods for Omics & Imaging data modalities:
Develop, productionize, and deploy cutting-edge ML approaches to integrate large-scale multi-modal phenotypic datasets;
Statistical and Translational Genetics:
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
Integrative Phenotyping:
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
Molecular Machine Learning:
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.
Computational Microscopy:
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.

Requirements

• 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.

Nice To Haves

• First-hand experience with biological data, preferably using computational approaches.
• Passion for learning how to work with diverse functional genomic assays (RNA/DNase/ATAC/ChIP-seq, etc).
• Interest in learning how to analyze single-cell RNA-seq data.
• Solid understanding of computational chemistry, including virtual screening (classic QSAR modeling, structure based drug-discovery), library design, etc.
• Demonstrated ability to use and develop cutting edge statistical and machine learning methods inspired by real problems.
• Experience with machine and deep Learning frameworks (e.g., scikit-learn, PyTorch, etc.).
• Demonstrated ability to write high-quality, production-ready code (readable, well-tested, with well-designed APIs).
• Experience in Linux environment, database languages (e.g., SQL, No-SQL) and version control practices and tools such as Git.
• Publications of high-quality work in relevant computational biology, bioinformatics, systems biology, life sciences, or biomedical venues, including journals and conferences.
• Passionate about solving problems, asking questions and learning independently.
• Familiarity with the SciPy/PyData ecosystem (numpy, pandas, scipy, dask etc.).
• Familiarity with cloud computing services (AWS or GCP).
• Familiarity with statistical analysis software, e.g., R.

Benefits

• Excellent medical, dental, and vision coverage as well as mental health and well-being support
• Access to free onsite baristas and daily lunch for interns who are onsite
• 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

What This Job Offers

• Career Level: Intern