Software Engineer Intern, Infrastructure (Summer 2027)

Socket.dev; San Mateo (CA); On-site; USD 31,000 - 42,000; Full time

Benefits offered by this job

Health benefits

401(k) plan with company match

Unlimited PTO

Parental leave

Wellness stipend

Learning & development stipend

Daily lunches & snacks

Relocation assistance

Job summary

DatologyAI in San Mateo, CA is seeking a Software Engineer Intern for Summer 2027 (May–August 2027). You’ll work with experienced engineers on large-scale data curation and model training infrastructure, gaining exposure to distributed systems, multi-cloud environments, and high-performance compute.

You will own meaningful projects that contribute to production systems and internal tooling, focusing on scale, reliability, and efficiency in modern AI workloads while collaborating with leading

Qualifications

• This internship is based in San Mateo and targets students pursuing CS/EE or related degrees.
• Strong coding skills in Python, Go, or C++ are required.
• Familiarity with Linux, Docker, Kubernetes, and cloud concepts is preferred.

Responsibilities

• 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 across multi-cloud and on-prem environments
• Collaborate with engineers and researchers to bring new ML infrastructure capabilities to production
• Participate in code reviews and technical discussions for scalable infrastructure

Skills

Python

Go

C++

Linux

Distributed systems

Education

Pursuing BS/MS/PhD in CS/EE or related

Tools

Docker

Kubernetes

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 stipendto 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.