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Datology AI

Software Engineer Intern, Infrastructure (Winter 2027)

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About this role

Join DatologyAI's Infrastructure team as a Software Engineer Intern (Winter 2027) to build and optimize large-scale data curation and AI training systems. You'll work on distributed infrastructure, multi-cloud deployment, and developer tooling alongside experienced engineers while learning how cutting-edge ML infrastructure operates at scale.

What you'll do

  • Build and enhance internal tools for developer productivity and system reliability
  • Design and prototype components of distributed training and data curation infrastructure
  • Develop automation, deployment, and observability systems across multi-cloud and on-premises environments
  • Collaborate with engineers and researchers to deploy new ML infrastructure capabilities to production
  • Participate in code reviews and technical discussions to learn scalable infrastructure best practices

What they're looking for

  • Python, Go, or C++
  • Linux systems administration
  • Docker and Kubernetes
  • Cloud platforms (AWS, Azure, or GCP)
  • Distributed systems design
  • Infrastructure automation
  • Observability and monitoring tools
  • Collaborative problem-solving

Benefits

  • 100% covered health insurance (medical, vision, dental)
  • 401(k) with 4% company match
  • Unlimited PTO
  • Relocation stipend for non-Bay Area candidates
  • $2,000 annual wellness stipend
  • $1,000 annual learning and development stipend
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Datology AI

DatologyAI builds a data curation platform that optimizes AI training datasets to reduce costs and improve model performance. The company is hiring cloud infrastructure engineers, data platform engineers, full-stack product engineers, and solutions engineers to scale its multi-cloud infrastructure and customer-facing tools.

View all jobs at Datology AI

Likely interview questions

  • Tell us about your experience with Docker and Kubernetes—what projects have you worked on?
  • How have you approached designing systems that need to scale to handle large workloads?
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