Spellbrush

Spellbrush

AI Infrastructure Engineer

San Francisco

Sponsorship not specified$20k-$910kDetected 896 days ago
Distributed SystemsRedisKubernetesKafkaMachine LearningLLMs

About the role

  • Spellbrush, the world's leading generative AI studio behind niji・journey, is looking for an AI Infrastructure Engineer to join us in building out end-to-end ML infrastructure to run our models on all platforms.

Responsibilities

  • Design, implement and run our next-generation inference architecture for running all our models powering all platforms and applications (mobile, web, etc.).
  • Work alongside a fast-paced and nimble team developing the latest state-of-the-art image generation models serving over 16 million users

Requirements

  • You have experience with large distributed systems
  • You have familiarity with the latest hotness like K8S, Kafka, NATS, Redis, etc.
  • You have an excellent understanding of GPU's handling large workloads

Nice to have

  • GPU workloads are different from traditional CPU workloads in very interesting ways.
  • Experience deploying, or even optimizing them end-to-end, is a huge plus for this role

Compensation

  • The final base salary is dependent upon location, experience, fit, and other factors. In addition, we offer a generous compensation package that includes equity, top-tier employer-sponsored health, dental, and vision insurance, and additional perks!

Benefits

  • In addition, we offer a generous compensation package that includes equity, top-tier employer-sponsored health, dental, and vision insurance, and additional perks!

Company info

  • At Spellbrush, we value creativity, collaboration, and innovation. If you're excited about working with cutting-edge technology and passionate about anime, gaming, and generative AI, we'd love to hear from you!
  • At Spellbrush, we value creativity, collaboration, and innovation.

Visa & Work Authorization

  • Visa sponsorships are available.

This listing is sourced directly from Spellbrush's careers page and normalized into a canonical job model.