Member of Technical Staff

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

At Gimlet, we believe every hire changes the company.

As a an early-stage company, talent density matters more than headcount. The engineers we hire today will shape the systems, culture, and standards that define Gimlet for years to come.

The future of AI infrastructure will not be built on a single hardware platform. It will be built on software capable of intelligently orchestrating increasingly heterogeneous compute to unprecedented scale.

Compilers sit at the center of that challenge. The performance gains unlocked at this layer compound across every workload that runs on the platform.

This role is an opportunity to help build the execution stack that transforms modern AI workloads into efficient programs running across diverse hardware architectures.

You will work across compiler infrastructure, runtime systems, scheduling, memory movement, kernel orchestration, and serving optimization to improve how AI workloads are executed in production.

This is not a traditional compiler role.

We are not building a language compiler in isolation.

We are building the systems that determine how AI workloads are partitioned, optimized, scheduled, and executed across the next generation of AI infrastructure.

You'll work on MLIR transformations, execution planning, speculative decoding optimization, heterogeneous scheduling, runtime optimization, and serving infrastructure that powers production AI workloads at scale.

To learn more about the kinds of systems we build, see our work on Corsair and low-latency speculative decoding:

see our work on Corsair and low-latency speculative decoding:

What success looks like

In your first 12-18 months, you will help:

• Build compiler and runtime infrastructure that improves latency, throughput, and efficiency for large-scale AI inference workloads.

• Design execution strategies that intelligently partition and coordinate workloads across heterogeneous hardware.

• Develop compiler optimizations spanning IR transformations, scheduling, memory movement, and kernel orchestration.

• Enable new model architectures and serving techniques to run efficiently in production environments.

• Influence the architecture of an execution platform that will help define how AI workloads are deployed over the next decade.

You may be a good fit if

• Strong systems and performance engineering fundamentals

• Experience building compiler systems, compiler-adjacent infrastructure, or execution/runtime systems

• Experience implementing IR transformations, compiler passes, lowering logic, or code generation systems

• Ability to reason about execution behavior, memory systems, scheduling, and hardware efficiency

• Strong software engineering skills in C++ and/or Python

• Bachelor's degree in a relevant field, or an equivalent combination of education, training, and professional experience.

Strong candidates may also have

• Experience with MLIR, LLVM, XLA, TVM, Triton, or similar compiler/runtime infrastructure

• Experience optimizing ML inference or serving workloads

• Familiarity with runtime systems, kernel dispatch, launch APIs, or memory allocators

• Experience working with GPUs, AI accelerators, or heterogeneous hardware systems

• Experience profiling and debugging performance-critical systems

• Familiarity with scheduling, partitioning, or kernel-level optimizations

Why join now?

Gimlet is at the very beginning of its journey, and that's what makes this moment special. Most AI infrastructure companies are focused on deploying more compute. We are focused on making increasingly diverse compute work together, and that ambition touches every part of how we build and run this company.

As an early member of the team, you will have significant ownership over your work, partner directly with a small group of highly capable people, and help shape not just what we build, but how we scale the company.

We value people who are excited to work across domains, take ownership of meaningful problems, and help define what Gimlet becomes over the next several years.