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Internship Tesla Computer Science Jobs in California

DEUNA

Data Science intern

San Francisco, CA · On-site

During the internship, you will support the Data team by analyzing data, building models ... Computer Science * Data Science * Statistics * Mathematics * Engineering * Economics * Physics * Or ...

DEUNA

Data Science intern

San Francisco, CA · On-site

During the internship, you will support the Data team by analyzing data, building models ... Computer Science * Data Science * Statistics * Mathematics * Engineering * Economics * Physics * Or ...

LLNL

Data Science Engineer

Livermore, CA · On-site

$121K - $154K/yr

Bachelor's degree in Computer Science, Data Science, Engineering, Mathematics, Statistics, Physics ... internships, or research projects). * Demonstrated experience developing generative AI solutions ...

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Internship Tesla Computer Science information

What is the difference between Internship Tesla Computer Science vs Software Engineering Intern?

AspectInternship Tesla Computer ScienceSoftware Engineering Intern
Required CredentialsEnrolled in Computer Science or related degree, coding skillsEnrolled in Software Engineering, Computer Science, or related degree, coding skills
Work EnvironmentResearch labs, development teams, collaborative projects at TeslaDevelopment teams, coding, testing, and debugging software at Tesla
Employer & Industry UsageTesla's tech and automotive industryTesla's software development teams within automotive and tech sectors
Search & Comparison IntentYes, often compared for tech internships at TeslaYes, similar search for software-focused internships at Tesla

Internship Tesla Computer Science focuses on foundational computer science principles and research, while Software Engineering Internships emphasize practical software development, coding, and debugging. Both roles are valuable for gaining experience at Tesla, but they differ in focus and daily tasks.

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Infographic showing various Internship Tesla Computer Science job openings in California as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 14% Part Time, 2% Contract, and 1% Nights. Highlights an 81% Physical, 2% Hybrid, and 17% Remote job distribution.

Scientist, Computational Biology

Arc Institute

Palo Alto, CA • On-site

Full-time

Re-posted 4 days ago

Job description

About Arc Institute
Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.
Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.
Our two Institute Initiatives reflect this model in action:
  • Virtual Cell Initiative: Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials.
  • Alzheimer's Disease Initiative: Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.

More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.
About the position
We are hiring for two Scientist positions on Arc's Computational Technology Center, each turning large-scale perturbational and single-cell datasets into mechanistic biological insight:
  1. Perturb-seq / functional genomics track - focused on large-scale CRISPR screen and Perturb-seq analysis, contributing to the Virtual Cell Initiative (VCI).
  2. Neurobiology / microglia track - focused on single-cell analysis of microglia and neurodegeneration, contributing to the Alzheimer's Disease Initiative (ADI).

Please indicate which focus area you're applying for in your application.
Situated at the interface of functional genomics, computational biology, and machine learning, successful candidates will analyze and model data from Perturb-seq, single-cell and multi-omic sequencing, lineage tracing, chemogenetic screens, and related high-throughput experimental approaches.
Both roles are highly collaborative, partnering closely with experimental scientists, bioinformatics infrastructure teams, machine learning researchers, and Arc investigators to identify biological mechanisms, nominate targets, and guide the design of future experiments.
About you
  • You want to understand biological mechanisms and are not satisfied with lists of differentially expressed genes; you want to understand why perturbations produce specific cellular outcomes.
  • You have deep hands-on experience with single-cell, perturbational, or multi-omic data and are comfortable working with large, messy biological datasets.
  • You think carefully about experimental design and enjoy collaborating across disciplines: you can discuss gene regulation, cellular identity, and assay design with experimentalists, and data models, statistics, and software with computational colleagues.
  • You have a solid understanding of existing computational methods and their limitations. When they are insufficient to answer the questions at hand, you can adapt and extend them.
  • You are excited by the opportunity to work in a mission-driven, open-science research institute with close ties to Stanford, UCSF, and UC Berkeley.
In this position you will
  • Conduct tertiary analyses of large-scale Perturb-seq, single-cell sequencing, multi-omic, and functional genomics datasets to identify functional relationships between genes, regulatory programs, and cellular phenotypes.
  • Depending on your track, this includes work such as e.g. guide assignment, perturbation-effect estimation, and interaction modeling for pooled CRISPR screens, or trajectory and cell-state analysis of microglial state transitions in neurodegeneration.
  • Partner with experimental teams on iterative study design, analysis, interpretation, and validation, discovering computational insights that translate into testable biological hypotheses.
  • Work closely with bioinformatics and data infrastructure teams to define clean handoffs from primary and secondary analysis into exploratory and mechanistic modeling.
  • Contribute to Arc's Virtual Cell and Alzheimer's Disease Initiatives by generating, analyzing, visualizing, and interpreting datasets that fuel predictive models.
  • Develop reusable analysis notebooks, dashboards, software tools, benchmarks, and data resources that allow Arc scientists to explore complex datasets effectively.
  • Present findings to internal stakeholders, and contribute to preprints or open-source projects when the opportunity arises or as needed.
  • Mentor colleagues and interns and contribute to a collaborative, intellectually rigorous team environment.
Requirements
  • PhD in computational biology, bioinformatics, genomics, systems biology, machine learning, computer science, molecular biology, or a related field, with 0-4 years of postdoctoral or professional research experience.
  • Demonstrated track record of deriving biological insight from large-scale single-cell, perturbational, or multi-omic datasets.
  • Hands-on experience with single-cell, perturbational, or multi-omic data, e.g. single-cell RNA-seq, Perturb-seq or CRISPR screens, single-cell ATAC-seq or multiome, including tertiary analysis, such as gene regulatory network inference, perturbation-effect or interaction modeling.
  • Expertise in at least one of the following:
    • (a) Perturb-seq / CRISPR screen analysis at scale, including guide assignment, perturbation-effect estimation, interaction modeling, batch correction, and interpretation of pooled genetic screens; or
    • (b) Single-cell analysis of microglia, in the context of neurodegeneration and Alzheimer's disease biology.
  • Python proficiency, with experience using modern scientific computing and data analysis ecosystems.
  • Familiarity with reproducible computational workflows, version control, and high-performance or cloud computing environments.
  • Biological intuition and the ability to collaborate effectively with experimental scientists.
  • Excellent written and verbal communication skills, with a track record of publications, preprints, open-source tools, or other scientific outputs.
  • Ability to work in a fast-paced, ambitious, interdisciplinary research environment.
  • Work a minimum of 3 days onsite in our Palo Alto office.
Preferred qualifications
  • Background in cell identity, reprogramming, RNA biology, cell engineering, neurobiology, immunology, cancer biology, or complex disease genetics.
  • Familiarity with dimensionality reduction and gene module analysis techniques in the context of single-cell biology.
  • Experience in a startup, technology center, research institute, or other highly collaborative environment where scientific direction and technical execution are tightly coupled.

The base salary range for this position is $135,000 to $186,500. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.

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