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Software Engineer, Infrastructure

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Software Engineer, Infrastructure in USA

  • DatologyAI
  • Full time
  • Email
  • San Mateo, CA

Responsibilities

  • Design and build reliable, secure development and production platforms at scale.
  • Architect, build, and deploy core infrastructure across multiple cloud providers and deployment models.
  • Develop data, training, and inference infrastructure for the company’s products and research capabilities.
  • Build excellent tooling and systems that improve productivity and developer experience.
  • Partner with founders, researchers, engineers, and teammates on product direction and technical decisions.
  • Debug networking, performance, hardware, memory, and other cross-stack problems while owning issues end to end.

Requirements

  • 4+ years of relevant experience.
  • Meaningful experience spearheading and constructing large-scale infrastructure.
  • Proficiency with Bash, Kubernetes, Python, and Terraform or similar technologies.
  • Experience with AWS, Azure or GCP, and/or on-premises environments.
  • Expertise debugging networking, performance, hardware, and memory issues across the stack.
  • Ability to build and operate scalable, reliable, and secure systems.
  • Preferred experience building data infrastructure from scratch or nearly from scratch at a fast-growing startup.
  • Preferred experience building ML/DL infrastructure or data infrastructure that feeds training of large ML models.

Benefits

  • 100% covered medical, vision, and dental benefits.
  • 401(k) plan with a 4% company match.
  • Unlimited paid time off.
  • 12 weeks of paid parental leave plus six months of work-from-home flexibility.
  • Annual $2,000 wellness stipend.
  • Annual $1,000 learning and development stipend.
  • Daily lunches and snacks provided in the office.
  • Relocation assistance for employees moving to the Bay Area.
  • Based in San Mateo, CA, with four days per week in the office.

Salary: $180k - $300k/yr

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-agnost...

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