---
title: 'Member of Technical Staff - Compiler Engineer at Gimlet Labs'
canonical: 'https://feeny.ai/job/member-of-technical-staff-compiler-engineer-gimlet-labs-san-francisco-tab2f7ke4cy9'
type: 'job'
last_seen: '2026-09-14'
---

# Member of Technical Staff - Compiler Engineer at Gimlet Labs

- **Company:** Gimlet Labs
- **Location:** San Francisco, CA
- **Compensation:** $180k–$400k
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-03-10
- **Last confirmed live:** 2026-09-14
- **Apply:** https://jobs.ashbyhq.com/gimlet/ac6c4998-d6e7-429d-a889-930376bcc9f2

## Job description

## 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 the compiler infrastructure that determines how AI workloads are represented, optimized, and executed across hardware with different architectures, performance characteristics, and memory systems. This is compiler engineering at the boundary of ML systems and distributed execution. The problems do not end when code is generated: compiler decisions interact directly with scheduling, communication, memory movement, kernel execution, and serving performance. You will work across intermediate representations, graph transformations, lowering, execution planning, and runtime interfaces. Compiler decisions directly shape where computation runs, how intermediate state moves between devices, which kernels execute, and ultimately the latency, throughput, and efficiency of the serving system. You will develop strategies for partitioning computation across devices, bring new models and accelerator architectures onto the platform, and partner with ML systems, kernel, and distributed systems engineers to improve end-to-end execution. Our work on [Corsair and low-latency speculative decoding](https://gimletlabs.ai/blog/low-latency-spec-decode-corsair) is one example of the problems this team tackles. What success looks like In your first 12-18 months, you will:

- Improve the latency, throughput, and efficiency of production inference workloads
- Design execution strategies for partitioning workloads across heterogeneous hardware
- Develop compiler optimizations spanning IR transformations, scheduling, memory movement, and kernel orchestration
- Enable new models, accelerator architectures, and serving techniques to run efficiently in production

You may be a good fit if you have

- Experience building compiler, runtime, or execution infrastructure
- Experience with IR transformations, compiler passes, lowering, or code generation
- Strong systems and performance-engineering fundamentals
- The ability to reason about execution behavior, memory systems, scheduling, and hardware efficiency
- Strong C++ and/or Python skills
- A bachelor’s degree in a relevant field or equivalent practical 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 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.

## About Gimlet Labs

## Company Overview
- **One-liner**: Gimlet Labs is an applied research lab building next-generation AI infrastructure, specifically a multi-silicon inference cloud that optimizes and orchestrates AI agent workloads across diverse hardware.
- **Entity Type**: Private (Series A)
- **Headquarters**: San Francisco, California, United States
- **Founded**: Not publicly available
- **Founders**: Zain Asgar (CEO), Michelle Nguyen, Natalie Serrino, Omid Azizi

## Core Business
- **Primary industry**: AI Infrastructure / Software Development
- **Target customers**: Large AI model labs, data centers, and enterprises running complex AI agent workloads (B2B, Enterprise).
- **Mission or purpose statement**: To build next-generation AI infrastructure that makes AI workloads 10x more efficient by intelligently orchestrating them across diverse hardware.

## Products & Services
- **Gimlet Cloud**: A serverless inference platform for AI agents. It allows users to run everything from simple agents to complex multi-agent systems with custom logic and data sources. The platform handles scheduling, orchestration, and optimization across diverse hardware. [gimletlabs.ai](https://gimletlabs.ai/)
- **kforge**: An autonomous kernel generation tool that creates optimized low-level kernels directly from PyTorch. It uses a multi-agent system to explore designs, enforce correctness, and identify the fastest kernels for backends like CUDA, ROCm, and Metal. [gimletlabs.ai](https://gimletlabs.ai/)
- **Multi-Silicon Inference Cloud**: A software layer that allows AI workloads to be simultaneously run across diverse types of hardware (CPUs, GPUs, high-memory systems), splitting work to use the best chip for each portion of the model. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)

## Market Standing
- **Valuation/Market Cap**: Not disclosed
- **Key Metric**: Total Funding of $92M (Seed: $12M led by Factory; Series A: $80M led by Menlo Ventures). The company publicly launched with eight-figure revenues (at least $10M). [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)
- **Notable Investors/Partners**: Menlo Ventures (Series A lead), Factory (Seed lead), Eclipse Ventures, Prosperity7, Triatomic. Angel investors include Sequoia’s Bill Coughran, Stanford Professor Nick McKeown, former VMware CEO Raghu Raghuram, and Intel CEO Lip-Bu Tan. Chip partners include NVIDIA, AMD, Intel, ARM, Cerebras, and d-Matrix. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)
- **Growth Signals**: The company publicly launched with eight-figure revenues (at least $10M). Customer base has more than doubled in the last four months, including a major model maker and a large cloud computing company. Headcount has grown 966.7% year-over-year to 25-30 employees. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/) [linkedin.com](https://www.linkedin.com/company/gimletlabs)

## Competitive Advantages
- **Multi-Silicon Orchestration**: The only platform that can simultaneously run AI workloads across diverse hardware (CPUs, GPUs, high-memory systems), splitting models to use the best chip for each portion. This claims to improve inference speed by 3x-10x for the same cost and power. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)
- **Autonomous Kernel Generation (kforge)**: Uses AI agents to automatically generate optimized low-level kernels from PyTorch, supporting CUDA, ROCm, and Metal without manual coding. [gimletlabs.ai](https://gimletlabs.ai/)
- **Deep Research Focus**: As an applied research lab, they are working on cutting-edge problems like universal AI compilers, SLA-aware scheduling, and headless hardware architectures, giving them a long-term technological moat. [gimletlabs.ai](https://gimletlabs.ai/)
- **Strong Founding Team**: Founders have deep expertise in systems and infrastructure, with a prior successful exit (Pixie Labs acquired by New Relic). [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)

## Strategic Focus
- **Productizing Research**: Translating cutting-edge research (universal AI compilers, autonomous kernel generation) into commercial products like Gimlet Cloud and kforge. [gimletlabs.ai](https://gimletlabs.ai/)
- **Scaling the Multi-Silicon Cloud**: Expanding their inference cloud to serve the largest AI model labs and data centers, with a focus on efficiency and cost reduction. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)
- **Talent Acquisition**: Aggressively hiring across AI research, kernel/GPU performance, compilers, distributed systems, and business operations to support rapid growth. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/gimlet)

## Why Work Here
- **Cutting-Edge Research & Engineering**: Work on fundamental AI infrastructure problems—autonomous kernel generation, universal AI compilers, and multi-silicon orchestration—alongside PhD-level researchers and top-tier engineers. [gimletlabs.ai](https://gimletlabs.ai/join_us)
- **High-Growth Environment**: The company has grown nearly 10x in headcount over the past year and is backed by top-tier VCs (Menlo Ventures, Sequoia angels). This is an early-stage opportunity with significant impact. [linkedin.com](https://www.linkedin.com/company/gimletlabs)
- **Fast-Moving Team**: Described as a "fast moving team with both AI research and industry expertise," looking for "builders" who love rapid prototyping. [gimletlabs.ai](https://gimletlabs.ai/join_us)
- **Location**: Based in San Francisco, California. The office is at 255 Potrero Ave. [linkedin.com](https://www.linkedin.com/company/gimletlabs)
- **Culture**: The company values researchers, builders, and strategists. The team includes alumni from Pixie Labs, Amazon, Google, AMD, Intel, and Groq. [linkedin.com](https://www.linkedin.com/company/gimletlabs)

## Sources
1. [gimletlabs.ai](https://gimletlabs.ai/)
2. [gimletlabs.ai/join_us](https://gimletlabs.ai/join_us)
3. [jobs.ashbyhq.com](https://jobs.ashbyhq.com/gimlet)
4. [linkedin.com](https://www.linkedin.com/company/gimletlabs)
5. [techcrunch.com](https://techcrunch.com/2026/03/23/startup-gimlet-labs-is-solving-the-ai-inference-bottleneck-in-a-surprisingly-elegant-way/)

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