Data Science & Machine Learning Intern

The Opportunity

Global drug development productivity is declining exponentially, 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 the data science & machine learning (DSML) team looking to work at the intersection of machine learning and life sciences for our Summer 2025 cohort. 

You will partner directly with a DSML team mentor in developing and/or applying ML methods to process and analyze large scale datasets from multiple modalities over the course of the summer (11-12 weeks). The DSML diverse team that works across the company spanning imaging, omics, statistical genetics, small molecule 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:
  • Build rich embedding models using DNA-Encoded Library (DEL) data on the scale of billions of compounds, and use these representations for downstream drug discovery tasks such as hit-discovery. 
  • Explore generative models of small molecules 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.

About You

• 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 Have

• 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.

Our target starting salary for successful US-based applicants for this role is $35 - $65 per hour. To determine starting pay, we consider multiple job-related factors including a candidate’s skills, education level and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.

In addition, insitro also provides our interns:

• Excellent medical, dental, and vision coverage as well as mental health and well-being support
• Access to free onsite baristas and cafe with daily lunch and breakfast for interns who are either onsite or hybrid
• Access to free onsite fitness center for interns who are either onsite or hybrid
• Access to a free commuter bus and ferry network that provides transport to and from our South San Francisco HQ from locations all around the Bay Area

insitro is an equal opportunity employer. All applicants will be considered for employment without attention to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran or disability status.

We believe diversity, equity, and inclusion need to be at the foundation of our culture. We work hard to bring together diverse teams–grounded in a wide range of expertise and life experiences–and work even harder to ensure those teams thrive in inclusive, growth-oriented environments supported by equitable company and team practices. All candidates can expect equitable treatment, respect, and fairness throughout the interview process.