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 a Member of Technical Staff focused on kernels and GPU performance. In this role, you will work close to accelerators and execution hardware to extract maximum performance from AI workloads across diverse and rapidly evolving platforms. You will analyze low-level execution behavior, design and optimize kernels, and ensure performance is reliable across both established and emerging hardware.

This role is ideal for engineers who enjoy deep performance work, reasoning about hardware tradeoffs, and turning theoretical peak performance into real-world results.

What you will work on

• Design, implement, and optimize GPU and accelerator kernels for AI workloads

• Analyze and tune performance across the GPU execution stack, including memory access patterns, synchronization, and instruction scheduling

• Work with compilers and runtimes to ensure kernels integrate cleanly and perform well in end-to-end systems

• Bring up and optimize execution on new or emerging accelerators

• Profile, benchmark, and debug performance issues across kernels, runtimes, and hardware

• Ensure performance optimizations are robust, correct, and production-ready at scale

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

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

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.