Gimlet Labs · San Francisco
Data & AIResearch and DevelopmentFullTimeposted 18mo ago
About us
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.
About the role
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.
What success looks like
In the first 12-18 months, you will:
Research, design, prototype, and evaluate novel approaches to improving AI model performance, efficiency, and reliability, with a focus on ideas that can be translated into production systems.
Stay current with the latest developments in AI systems research and rapidly assess emerging techniques for their practical impact.
Become a core contributor on one of Gimlet's product engineering teams, writing production-quality code that powers customer-facing AI infrastructure.
Build experimental frameworks to validate approaches such as fine-tuning, knowledge distillation, KV caching, optimized attention variants, and other inference optimization techniques.
You may be a good fit if you
Enjoy balancing exploratory research with hands-on production engineering
Are comfortable reading research, designing experiments, and evaluating competing approaches
Have experience applying AI or machine-learning techniques to practical engineering problems
Thrive in environments where rapid prototyping and iteration are essential
Strong candidates may also have
Experience with modern AI frameworks such as PyTorch, TensorFlow, vLLM, ONNX, or similar tools.
Strong software engineering skills in Python and C++, with experience building production-quality systems.
Familiarity with modern AI systems research, including inference optimization, fine-tuning, knowledge distillation, or efficient model serving.
A solid foundation in statistics and experimental design for evaluating model performance.
Experience translating research prototypes into production-ready software.
Why join now?
Gimlet is expanding from its core technology into a production neocloud spanning new hardware, customers, and data centers.
Solve hard problems.
Own meaningful work.
Build for production.
Help define what’s next.
Agency Policy: Gimlet Labs does not accept unsolicited resumes from recruitment agencies or search firms. Any unsolicited resumes submitted without a signed agreement will be considered the property of Gimlet Labs, and no fees will be paid.