Member of Technical Staff - Distributed Systems
San Francisco, CA$150k - $390kFull-timePosted 3mo ago
About the role
Gimlet is building a production neocloud for AI inference. The role focuses on building systems to schedule, route, and coordinate AI workloads across Gimlet’s heterogeneous infrastructure, enabling distributed inference pipelines and reliable operation across multiple accelerators, nodes, and data centers.
Responsibilities
- Build scheduling and orchestration systems for heterogeneous compute
- Design systems that manage independently scalable stages of distributed inference pipelines
- Improve the reliability and fault tolerance of production AI infrastructure
- Develop control planes and APIs that simplify how workloads are deployed and managed
- Improve resource management and scheduling as Gimlet expands across new accelerator types, nodes, and data centers
- Help the platform scale across additional hardware, nodes, and data centers
Qualifications
- Experience building or operating distributed systems in production
- Strong software-engineering and systems fundamentals
- Ability to reason about concurrency, consistency, failure modes, and system tradeoffs
- Experience with scheduling, resource management, RPC, or asynchronous messaging
- A bachelor’s degree in a relevant field or equivalent practical experience
Skills
Required:Distributed systemsConcurrency and fault toleranceScheduling / resource management / RPC / asynchronous messaging
Nice to have:Go / C++ / PythonKubernetes or Kubernetes-adjacent systems
Last updated Oct 11, 2026. Interested? Apply on the company site.
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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