Member of Technical Staff - AI Research (Intern)

🇺🇸 San Francisco, California
$0 - $0 Hourly
Posted 1 day ago
Expires August 8, 2026

About Us

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.

ABOUT THE ROLE

Gimlet Labs is seeking an Member of Staff focused on AI Research (Intern). As an AI Researcher (Intern), you will be evaluating and implementing techniques to drive performance and quality optimizations across the latest AI models. The research team is responsible for exploring new model architectures and experimenting with novel inference efficiency techniques such as KV caching and FlashAttention. The team will design and prototype frameworks leveraging fine-tuning and knowledge distillation to push the boundaries of model performance.

WHAT YOU WILL WORK ON

- Monitoring and evaluating cutting-edge AI research

- Researching ways to improve model accuracy, performance and efficiency

- Prototyping frameworks with the latest fine-tuning and distillation techniques 

YOU MAY BE A GOOD FIT IF

- Currently pursuing degree in computer science, engineering, or comparable area of study

- Experience with AI/ML or distributed systems.

STRONG CANDIDATES MAY ALSO HAVE

- Experience with PyTorch, TensorFlow, ONNX and other AI frameworks

- Familiarity with distributed systems and o...

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