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Datology AI

Software Engineer Intern, Infrastructure (Summer 2027)

Datology AI19 Sep 2026
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Listed location: San Mateo

Work arrangement: onsite. A remote label does not confirm worldwide eligibility or visa sponsorship.

Read the employer’s description for qualifications, compensation and work eligibility. Confirm the position is still open on the application page.

Job description supplied by Datology AI; category and skill labels may be inferred. How our listings work · Report a problem

Job Description

About the Company

Models are what they eat. But a large portion of training compute is wasted training on data that are already learned, irrelevant, or even harmful, leading to worse models that cost more to train and deploy.

At DatologyAI, we’ve built a state of the art data curation suite to automatically curate and optimize petabytes of data to create the best possible training data for your models. Training on curated data can dramatically reduce training time and cost (7-40x faster training depending on the use case), dramatically increase model performance as if you had trained on >10x more raw data without increasing the cost of training, and allow smaller models with fewer than half the parameters to outperform larger models despite using far less compute at inference time, substantially reducing the cost of deployment. For more details, check out our recent research on synthetic data scaling (BeyondWeb) and pretraining with domain-specific data (The Finetuner’s Fallacy).

We raised a total of $57.5M in two rounds, a Seed and Series A. Our investors include Felicis Ventures, Radical Ventures, Amplify Partners, Microsoft, Amazon, and AI visionaries like Geoff Hinton, Yann LeCun, Jeff Dean, and many others who deeply understand the importance and difficulty of identifying and optimizing the best possible training data for models. Our team has pioneered this frontier research area and has the deep expertise on both data research and data engineering necessary to solve this incredibly challenging problem and make data curation easy for anyone who wants to train their own model on their own data.

This role is based in San Mateo, CA. We are in office 4 days a week.

About the Role

We are looking for a Software Engineer Intern to join our Infrastructure team for Summer 2027, with the internship taking place sometime between May and August 2027. You’ll work closely with experienced engineers to design and build the systems that power large-scale data curation and model training. This is an opportunity to learn how cutting-edge AI infrastructure is built from the ground up—across distributed systems, multi-cloud environments, and high-performance compute platforms.

As an intern, you’ll take ownership of meaningful projects that contribute directly to our production systems and internal tooling. You’ll learn how to think about scale, reliability, and efficiency in the context of modern AI workloads, while collaborating with some of the strongest engineers and researchers in the field.

What You'll Work On

  • Build and improve internal tools that accelerate developer productivity and system reliability

  • Design and prototype components of DatologyAI’s distributed training and data infrastructure

  • Contribute to automation, deployment, and observability systems across multi-cloud and on-prem environments

  • Collaborate with engineers and researchers to bring new ML infrastructure capabilities to production

  • Participate in code reviews, technical discussions, and learn best practices for scalable infrastructure development

About You

  • Pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field

  • Strong programming skills in Python, Go, or C++

  • Familiar with Linux systems, Docker, Kubernetes, or similar technologies

  • Curious about cloud computing (AWS, Azure, or GCP) and large-scale distributed systems

  • Excited to learn how infrastructure enables ML research and model deployment at scale

  • Collaborative, detail-oriented, and eager to take on complex technical problems

Compensation

This is a paid internship with a standard monthly stipend based out of our San Mateo office. If you are not currently located in the Bay Area, we provide a relocation stipend to help cover travel and living expenses during your three months on-site.

Benefits:

  • 100% covered health benefits (medical, vision, and dental).

  • 401(k) plan with a generous 4% company match.

  • Unlimited PTO policy

  • Paid Parental Leave of 12 weeks, plus 6 months of WFH flexibility.

  • Annual $2,000 wellness stipend.

  • Annual $1,000 learning and development stipend.

  • Daily lunches and snacks are provided in our office!

  • Relocation assistance for employees moving to the Bay Area.

Skills mentioned

PythonKubernetesDockerAWSGCPAzureGoC++

Frequently asked questions

Is the Software Engineer Intern, Infrastructure (Summer 2027) position at Datology AI remote?

The Software Engineer Intern, Infrastructure (Summer 2027) role at Datology AI does not have a confirmed remote arrangement in our data. Check the employer description for its work location.

What type of employment is the Software Engineer Intern, Infrastructure (Summer 2027) role?

Datology AI is hiring for a internship Software Engineer Intern, Infrastructure (Summer 2027) position.

Which skills are mentioned for the Software Engineer Intern, Infrastructure (Summer 2027) job at Datology AI?

Detected skill labels include Python, Kubernetes, Docker, AWS, GCP, Azure, Go, C++. Check the employer description to distinguish required skills from preferred experience.

How do I apply for the Software Engineer Intern, Infrastructure (Summer 2027) position at Datology AI?

You can apply for the Software Engineer Intern, Infrastructure (Summer 2027) role directly through Datology AI's official application link provided on this page.

Interested in this role?

Apply directly on the company's website.

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Datology AI

Datology AI

Website
Posted19 Sep 2026
Typeinternship
LevelInternship
LocationSan Mateo
Apply NowView All Jobs at Datology AI

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