About the role
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, you will build and optimize the low-level execution primitives that turn accelerator performance into production inference performance.
Rather than optimizing for one hardware architecture, you will work across accelerators with different execution models, memory hierarchies, capabilities, and software stacks. Your work will shape the latency, throughput, and efficiency Gimlet can achieve across established and emerging hardware architectures.
You will work close to the hardware across kernel implementation, memory access, execution behavior, profiling, and performance validation. You will develop optimizations that account for differences between accelerator architectures and partner with compiler, ML systems, and distributed systems engineers to improve performance across the full execution stack.
What success looks like
In the first 12-18 months, you will:
- Build and optimize kernels that improve latency, throughput, and hardware utilization for production AI workloads
- Develop execution strategies that unlock performance across both established and emerging accelerator architectures
- Improve memory efficiency, scheduling behavior, and execution characteristics across the inference stack
- Partner with compiler, runtime, and distributed systems engineers to ensure end-to-end performance optimization
- Influence how heterogeneous hardware is deployed and utilized within the next generation of AI infrastructure
- Help establish performance engineering standards that shape the future of Gimlet's execution platform
You may be a good fit if
- Strong software engineering fundamentals
- Experience working on performance-critical systems close to hardware
- Comfort reasoning about low-level execution behavior, memory hierarchies, and performance tradeoffs
- Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.
Strong candidates may also have
- Experience with CUDA, Triton, CUTLASS, or other accelerator programming models
- Deep understanding of GPU execution models (warps/wavefronts, blocks, grids)
- Experience optimizing memory access patterns (coalescing, shared memory, cache behavior)
- Familiarity with occupancy, latency hiding, and instruction-level parallelism
- Experience using profiling and performance analysis tools
- Familiarity with multi-GPU or distributed execution is a plus
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.