Gimlet Media

Member of Technical Staff - AI Research

San Francisco, CA · Staff+

Sponsorship not specifiedDetected 511 days ago

PythonC++Distributed SystemsFull-Stack DevelopmentMachine LearningTensorFlowPyTorchStatisticsResearch

About the role

Gimlet Labs is seeking an Member of Technical Staff focused on AI research.
The research team is responsible for exploring new model architectures and experimenting with novel inference efficiency techniques such as KV caching and FlashAttention.
Monitoring and evaluating cutting-edge AI research

As an AI Researcher, you will be evaluating and implementing techniques to drive performance and quality optimizations across the latest AI models.
Our team operates across datacenters, networking, distributed systems, compilers, runtimes, orchestration, and performance engineering to build the foundation for the next generation of AI infrastructure.
As an early member of the team, you will have significant ownership, work alongside highly technical engineers, and help shape both the systems we build and how we scale the company.
We value people who are excited to work across domains, take ownership of meaningful problems, and build technology that enables the next generation of AI.

Master's or PhD degree in computer science, engineering, applied mathematics or comparable area of study
Experience with AI/ML or applied data science.
Experience with PyTorch, TensorFlow, vLLM, ONNX and other AI frameworks
Software development experience with Python and C++

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.

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