Member of Technical Staff - Applied AI Research

About Us Gimlet is building the first multi-silicon neocloud designed for fast, efficient inference. 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. This gives our team access to systems research problems grounded in frontier models, cutting-edge production workloads, and emerging hardware architectures. ABOUT THE ROLE Gimlet Labs is seeking a Member of Technical Staff focused on Applied AI research. As an AI Researcher, you will research, evaluate, and implement techniques that improve the performance and efficiency of AI workloads across heterogeneous hardware. The research team is responsible for exploring new model architectures and experimenting with novel inference efficiency techniques such as KV caching and optimized attention variants. The team will design and prototype frameworks leveraging fine-tuning and knowledge distillation to push the boundaries of model performance. 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 hands-on software engineering with exploratory research, and are excited to spend roughly half your time building production systems and half investigating new ideas. - You are comfortable reading research papers, designing experiments, and using data to evaluate competing approaches. - You have experience applying AI/ML techniques to solve practical engineering problems. - You enjoy working in ambiguous 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. WHAT MAKES GIMLET DIFFERENT At Gimlet, you will work on infrastructure problems that span the full stack of modern AI systems. 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. Why join now? Gimlet is…