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 RoleWe’re looking for a Software Engineer Intern (Fall 2026) to join our Infrastructure team at DatologyAI. 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 OnBuild 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
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
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.
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What We Do
DatologyAI builds tools to automatically select the best data on which to train deep learning models. Our tools leverage cutting-edge research—much of which we perform ourselves—to identify redundant, noisy, or otherwise harmful data points. The algorithms that power our tools are modality-agnostic—they’re not limited to text or images—and don’t require labels, making them ideal for realizing the next generation of large deep learning models. Our products allow customers in nearly any vertical to train better models for cheaper.
