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Gimlet Labs is seeking a Member of Technical Staff focused on Applied AI Research in San Francisco to advance AI workloads from concept to production. You will explore novel techniques, design experiments, and translate promising ideas into scalable software.
You will work across research and engineering to build prototypes and deliver production-quality code, applying methods such as fine-tuning, knowledge distillation, and optimized inference across hardware platforms.
Gimlet is building the first multi-silicon neocloud designed for fast, efficient AI inference.
We combine large-scale compute infrastructure with an execution platform that partitions AI workloads and maps each stage to the hardware best suited to run it.
We work with foundation labs, hyperscalers, and AI-native companies, giving our team access to technical problems spanning frontier models, production infrastructure, and emerging hardware.
As a Member of Technical Staff focused on Applied AI Research, you will explore techniques that improve the quality, performance, and efficiency of AI workloads and translate the most promising ideas into production systems.
You will work across research and engineering: reading and evaluating new work, designing experiments, building prototypes, and contributing production-quality code. Your research may include model architectures, fine-tuning, knowledge distillation, KV caching, attention mechanisms, and other techniques that improve inference across different hardware platforms.
In the first 12-18 months, you will:
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
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