Software Engineer Intern, Machine Learning (Fall 2026)

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 blog posts sharing our high-level results for text models and image-text models.

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 Redwood City, CA. We are in office 4 days a week.

About the Role

We’re looking for a Software Engineer Intern to join our Machine Learning team at DatologyAI during Fall 2026. You’ll work alongside experienced engineers and researchers to design, prototype, and deploy the ML systems that power our data curation platform. Our work sits at the intersection of large-scale infrastructure, machine learning research, and data quality — building systems that make model training faster, cheaper, and smarter.

As an intern, you’ll take on impactful projects that touch real production workflows, from scaling data pipelines to implementing experimental research ideas. You’ll gain hands-on experience building ML systems in a fast-paced, deeply technical environment while learning how world-class teams approach AI infrastructure.

What You'll Work On

• Build and improve components of our ML training and data curation pipelines

• Prototype and evaluate algorithms that identify informative data samples at scale

• Work with researchers to bring new data selection and model evaluation techniques into production

• Contribute to reliable and efficient distributed ML systems

• Learn how to take an idea from research to real-world deployment

About You

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

• Strong programming skills in Python and familiarity with libraries like PyTorch, TensorFlow, or JAX

• Solid understanding of data structures, algorithms, and ML fundamentals

• Curious about large-scale training systems, data curation, and the infrastructure behind AI models

• Eager to learn from experienced engineers and contribute to production-quality code

• Collaborative, detail-oriented, and driven by curiosity

Compensation

This is a paid internship with a standard monthly stipend. 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.

We offer a comprehensive benefits package to support our employees' well-being and professional growth:

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

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

• Unlimited PTO policy

• 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.