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• Field AI

How Did You Hear About Us?

• How did you learn about this opportunity?✱

  • Field AI Website
  • Employee Referral
  • LinkedIn
  • Indeed
  • ZipRecruiter
  • School or University
  • Professional Organization
  • YouTube, Facebook, or Instagram
  • "Analytics Insight", "AI Magazine" or other AI publication
  • AI Search Engine, including You.com, Perplexity AI, or other site
  • Referral from a friend, colleague, or professor
  • Other job website

Start Date

• Please let us know you would be able to begin a new opportunity.✱

  • I can start immediately.
  • I can start in two to three weeks.
  • I can start in a month.
  • I can start in six weeks to two months.
  • I can start in eight to twelve weeks.

Are you authorized to work in the U.S.?

• Are you authorized to work in the United States?✱

  • Yes
  • No
• Will you now, or in the future, require sponsorship (i.e. H-1B visa, etc.) to legally work in the U.S.?✱

  • Yes
  • No
  • Not applicable
• Are you currently in a period of Optional Practical Training (OPT)?✱

  • Yes
  • No
  • Not applicable
• Do you hold a degree in a Science, Technology, Engineering, or Mathematics (STEM) field from a U.S. accredited institution that would allow you to apply for a 24-month OPT extension?✱

  • Yes
  • No
  • Not applicable

Work Location Availabilities

• Are you able to work onsite?✱

  • Yes - everyday
  • Yes - Hybrid - a few days a week
  • No - 100% remote
• Which location can you work from?

  • Mission Viejo, CA
  • Seattle, Washington
  • The Bay Area, CA

1.61 ML Infrastructure Engineer — ML Platform, Tooling & Systems

• 1. Can you describe your experience building and maintaining ML infrastructure and developer tooling, specifically in supporting the entire ML lifecycle from data ingestion to model deployment?
• 2. How familiar are you with containerization technologies like Docker and orchestration tools such as Kubernetes, and how have you used them to improve ML development environments?
• 3. Please discuss your experience with CI/CD workflows and infrastructure-as-code (e.g., Terraform, AWS CDK) in the context of ML systems.
• 4. Can you provide an example of a time you collaborated with ML scientists, software engineers, or roboticists to translate their platform needs into robust engineering solutions?
• 5. This role involves working with a shared monorepo and leveraging systems like Bazel for builds and tests. Can you speak to your experience with similar systems and how you've contributed to improving developer experience in such environments?

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