Gimlet Media
Member of Technical Staff - Distributed Systems
San Francisco, CA · Staff+
Sponsorship not specifiedDetected 134 days ago
PythonC++Distributed SystemsKubernetes
About the role
- At Gimlet, we believe every hire changes the company.
- As a an early-stage company, talent density matters more than headcount.
- The engineers we hire today will shape the systems, culture, and standards that define Gimlet for years to come.
Responsibilities
- You will design and operate the distributed systems that schedule, route, and coordinate AI workloads across thousands of nodes and diverse hardware architectures.
- This role is an opportunity to help build that future.
- Build scheduling and orchestration systems that coordinate workloads across heterogeneous hardware
- The systems we build today will help define how AI workloads are deployed for the next decade.
Requirements
- Comfort reasoning about concurrency, failure modes, and tradeoffs in large-scale systems
- Experience with Kubernetes or Kubernetes-adjacent systems beyond basic usage
- Experience designing service-oriented architectures using RPC or asynchronous messaging
- Familiarity with scheduling, queues, or resource management systems
- Software development experience in languages commonly used for systems development (e.g., Go, C++, Python)
Company info
- Gimlet is building the next generation of AI infrastructure: large-scale AI datacenters and the orchestration platform that coordinates them.
- The future of AI will require vastly more compute than exists today. But as AI workloads become more complex and new hardware architectures emerge, simply deploying more GPUs isn't enough. The challenge is making increasingly diverse compute work together.
- Gimlet's platform intelligently partitions and routes workloads across heterogeneous hardware, enabling step-function improvements in performance and efficiency. Customers deploy through production-grade APIs without needing to think about hardware selection, placement, or optimization.
- We work with foundation labs, hyperscalers, and AI-native companies to power production workloads at massive scale and help define the infrastructure layer for the future of AI.
- The future of AI will require vastly more compute than exists today.
- But as AI workloads become more complex and new hardware architectures emerge, simply deploying more GPUs isn't enough.
- The challenge is making increasingly diverse compute work together.
- Gimlet's platform intelligently partitions and routes workloads across heterogeneous hardware, enabling step-function improvements in performance and efficiency.
- Customers deploy through production-grade APIs without needing to think about hardware selection, placement, or optimization.
- large-scale AI datacenters and the orchestration platform that coordinates them.
- As a an early-stage company, talent density matters more than headcount. The engineers we hire today will shape the systems, culture, and standards that define Gimlet for years to come.
- We are not optimizing for headcount, we are optimizing for talent density.
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This listing is sourced directly from Gimlet Media's careers page and normalized into a canonical job model.