---
title: 'Data Center Facilities Operations Lead at Gimlet Labs'
canonical: 'https://feeny.ai/job/data-center-facilities-operations-lead-gimlet-labs-san-francisco-btk5mb65ej0p'
type: 'job'
last_seen: '2026-09-07'
---

# Data Center Facilities Operations Lead at Gimlet Labs

- **Company:** Gimlet Labs
- **Location:** San Francisco, CA
- **Compensation:** $150k–$235k
- **Employment:** full-time
- **Work type:** remote
- **Posted:** 2026-08-13
- **Last confirmed live:** 2026-09-07
- **Apply:** https://jobs.ashbyhq.com/gimlet/17988d93-9f1c-49b9-bd35-13e407ec46af

## 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

Gimlet Labs is seeking a Data Center Facilities Operations Lead to own the critical facilities operating model for Gimlet data centers and high-density AI infrastructure deployments. In this role, you will make sure the facility-side systems that support Gimlet's compute capacity are ready, monitored, maintained, and operating inside the required envelope.

You will focus on the infrastructure that keeps liquid-cooled AI systems healthy: facility water loops, CDUs, supply and return temperatures, flow, pressure, water quality, leak detection, alarms, heat rejection, power and cooling coordination, BMS/DCIM telemetry, maintenance procedures, and vendor repair workflows.

This role is well-suited for a critical facilities operator who understands data center MEP systems, liquid cooling, operational monitoring, and the discipline required to keep high-density compute environments stable as Gimlet scales.

What success looks like

In the first 12-18 months, you will:

- Build the facilities operations model for current and future Gimlet sites, including operating standards, escalation paths, maintenance routines, acceptance criteria, and facility readiness gates.
- Translate OEM and engineering requirements for liquid-cooled platforms into practical site operating envelopes for temperature, flow, pressure, water quality, alarms, and heat rejection.
- Own monitoring and response for facility-side telemetry, including supply and return water temperatures, delta-T, flow, pressure, leak detection, CDU status, cooling capacity margins, and BMS/DCIM alarms.
- Partner with colocation providers, facility vendors, OEMs, Site Managers, Data Center Technicians, Deployment Leads, and TPMs to ensure facilities are ready before new compute capacity is deployed.
- Create and maintain MOPs, SOPs, EOPs, maintenance windows, runbooks, inspection routines, and incident response procedures for critical facilities and liquid cooling operations.
- Coordinate preventive maintenance, repairs, and vendor response for CDUs, facility water loops, filters, valves, pumps, sensors, leak detection systems, chillers, dry coolers, CRAHs, and related infrastructure.
- Lead facility-side root cause analysis for thermal, leak, power, cooling, monitoring, and environmental events, then drive durable corrective actions.
- Build reporting that shows facility health, risk, readiness, capacity margin, recurring issues, open repairs, and operational trends across Gimlet sites.

You may be a good fit if you have

- Experience in data center facilities operations, critical facilities engineering, MEP operations, commissioning, or facilities maintenance
- Experience operating liquid-cooled, high-density compute infrastructure
- Familiarity with facility water systems, CDUs, heat rejection, leak detection, and water-quality controls
- Experience using BMS, DCIM, EPMS, or similar systems to monitor and respond to facility conditions
- The ability to create and execute operational procedures with strong attention to safety and reliability
- Experience coordinating across site teams, colocation providers, OEMs, and facilities vendors
- The ability to work in active data center environments and support urgent facilities escalations

Strong candidates may also have

- Experience supporting GPU, HPC, or rack-scale liquid-cooled infrastructure
- Experience with commissioning, integrated systems testing, site acceptance, or facility turnover
- Familiarity with power distribution, UPS and generator systems, chilled water, dry coolers, CRAH/CRAC systems, or rear-door heat exchangers
- Experience managing colocation obligations, service levels, maintenance windows, and vendor escalations
- A track record of improving facility reliability through monitoring, preventive maintenance, and incident analysis

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/)

## Other roles at Gimlet Labs

- [Executive Business Partner](https://feeny.ai/job/executive-business-partner-gimlet-labs-san-francisco-5z7qmxc97ejn) — San Francisco, CA
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