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Member of Technical Staff - Infrastructure

Gimlet Labs· Research and Development
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San Francisco, CA$150k - $390kFull-timePosted 3mo ago

About the role

Infrastructure Platform Engineer role building production infrastructure for Gimlet's AI cloud, turning heterogeneous accelerator hardware into reliable production systems, spanning bare metal to Kubernetes and observability.

Responsibilities

  • Deploy and operate production clusters across different accelerator architectures
  • Automate hardware provisioning, validation, upgrades, and fleet lifecycle management
  • Improve cluster scheduling, resource utilization, isolation, and capacity management
  • Build observable infrastructure that enables faster debugging, incident response, and recovery
  • Partner across distributed systems, runtime, compiler, networking, and hardware teams to bring new accelerators into production

Qualifications

  • Experience in infrastructure, cluster engineering, platform engineering, SRE, or HPC
  • Strong Linux systems knowledge and production debugging experience
  • Experience operating Kubernetes, Slurm, Nomad, or similar orchestration systems
  • Experience automating infrastructure with Python, Go, Terraform, Ansible, or similar tools
  • Experience with GPU or accelerator infrastructure, including drivers, firmware, or CUDA/ROCm
  • The ability to build systems that are observable, recoverable, and reliable in production
  • A bachelor’s degree in a relevant field or equivalent practical experience

Skills

Required:Infrastructure engineeringLinuxKubernetes / orchestrationAutomation toolingGPU / accelerator infra

Benefits

  • -
Last updated Oct 11, 2026. Interested? Apply on the company site.
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About Gimlet Labs

Gimlet Labs

Gimlet Labs is an applied research lab focused on building high performance AI systems.

Autonomous kernel generation for heterogeneous hardwareCost-aware optimization frameworks for AI workloadsGimlet cloudHeadless hardware architectures for serving AI inferenceHybrid edge/cloud workload partitioning and orchestrationSLA-aware dynamic datacenter scheduling of AI agent workloadsUniversal AI compiler for heterogeneous hardwarebessemer-ai-infrastructure-2026

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