Software Engineer Intern, Infrastructure (Winter 2027)
DatologyAI2 hours ago
San Mateo, CA, USAIntern
H1B Sponsor
Responsibilities
- Build and improve internal tools that accelerate developer productivity and system reliability.
- Design and prototype components of distributed training and data infrastructure.
- Contribute to automation, deployment, and observability systems across multi-cloud and on-premises environments.
- Collaborate with engineers and researchers to bring new ML infrastructure capabilities to production.
- Participate in code reviews and technical discussions while learning scalable infrastructure development practices.
Requirements
- Pursuing a BS, MS, or PhD in Computer Science, Electrical Engineering, or a related field.
- Strong programming skills in Python, Go, or C++.
- Familiarity with Linux systems, Docker, Kubernetes, or similar technologies.
- Curiosity about cloud computing, including AWS, Azure, or GCP, and large-scale distributed systems.
- Interest in how infrastructure enables machine learning research and model deployment at scale.
- Collaborative, detail-oriented, and eager to take on complex technical problems.
Benefits
- Paid three-month internship based on-site at the San Mateo office, scheduled sometime between January and April 2027.
- Relocation stipend and assistance for interns moving to the Bay Area.
- 100% covered medical, vision, and dental benefits.
- 401(k) plan with a company match.
- Unlimited PTO.
- Paid parental leave and WFH flexibility.
- Annual wellness and learning and development stipends.
- Daily office lunches and snacks.
Categories
About DatologyAI
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.