---
title: 'Member of Technical Staff - GPU Infrastructure Engineer at Liquid AI'
canonical: 'https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-engineer-liquid-ai-san-francisco-5qry0efpcqvv'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff - GPU Infrastructure Engineer at Liquid AI

- **Company:** Liquid AI
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-07-28
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/liquid-ai/ec3912e6-3751-4b5a-8769-4509bdd750c4

## Job description

## ABOUT LIQUID AI

Spun out of MIT CSAIL, we build general-purpose AI systems that run efficiently across deployment targets, from data center accelerators to on-device hardware, ensuring low latency, minimal memory usage, privacy, and reliability. We partner with enterprises across consumer electronics, automotive, life sciences, and financial services. We are scaling rapidly and need exceptional people to help us get there.

## THE OPPORTUNITY

Our Cluster Infrastructure team owns the compute environments that power foundation model training and research at Liquid AI. We are looking for a hands-on software engineer to keep our GPU clusters reliable, improve resource efficiency, and build the tooling that allows researchers to focus on model development rather than infrastructure.

This role matters because infrastructure issues can delay training by days, while improvements in utilization, storage management, and automation can significantly increase research velocity and reduce compute costs. You will work closely with researchers and infrastructure engineers, owning problems from immediate operational response through long-term platform improvements.

## WHAT WE’RE LOOKING FOR

We need someone who:

- Brings order to complex systems: You identify root causes and build durable fixes rather than repeatedly firefighting.
- Is an engineer first: You can go deep across Linux, networking, storage, schedulers, and distributed systems.
- Balances operations and engineering: You handle urgent issues while steadily replacing manual work with automation.
- Owns outcomes: You communicate clearly, prioritize effectively, and drive problems to resolution across internal teams and external providers.

## THE WORK

- Own the reliability and operation of the GPU clusters used for training and research.
- Debug issues across compute, storage, networking, schedulers, and distributed workloads.
- Improve CPU, GPU, and storage utilization through better tooling and automation.
- Onboard and migrate workloads across GPU providers and hardware platforms.
- Build monitoring, validation, and platform abstractions that reduce operational work for researchers.
- Contribute to the longer-term architecture of Liquid AI’s training infrastructure and GPU platform.

## DESIRED EXPERIENCE

## MUST-HAVE

- Strong software engineering experience, with the ability to build production-quality infrastructure tooling and automation.
- Deep knowledge of distributed systems, Linux, networking, and storage.
- Experience operating a shared compute cluster or distributed training platform.
- A track record of supporting production users and turning recurring failures into durable solutions.
- The technical depth to partner effectively with senior research and infrastructure engineers.

## NICE-TO-HAVE

- Experience with SLURM, Kubernetes, Ray, Hadoop, or another distributed compute platform.
- Experience supporting GPU, HPC, or large-scale AI training infrastructure.
- Experience with distributed storage, cluster schedulers, cloud providers, or infrastructure control planes.

## WHAT SUCCESS LOOKS LIKE (YEAR ONE)

1. Researchers spend less time resolving infrastructure and resource-allocation issues.
2. GPU, CPU, and storage resources are used more efficiently across the fleet.
3. Recurring operational problems are replaced with automation, monitoring, and dependable platform tooling.
4. Liquid AI has the beginnings of a durable internal platform that hides infrastructure complexity from researchers.

## WHAT WE OFFER

- High-impact ownership: Own infrastructure that directly affects how quickly and efficiently we train foundation models.
- Compensation: Competitive base salary with equity in a unicorn-stage company.
- Health: We pay 100% of medical, dental, and vision premiums for employees and dependents.
- Financial: 401(k) matching up to 4% of base pay.
- Time Off: Unlimited PTO plus company-wide Refill Days throughout the year.

## About Liquid AI

## Company Overview
- **One-liner**: Liquid AI builds efficiency-first, general-purpose foundation models designed to run on-device and at the edge, delivering state-of-the-art performance with minimal compute.
- **Entity Type**: Private (Series A)
- **Headquarters**: Cambridge, Massachusetts, United States
- **Founded**: 2023
- **Founders**: Ramin Hasani (CEO), Mathias Lechner (CTO), Alexander Amini (CSO), Daniela Rus

## Core Business
- **Primary Industry**: Artificial Intelligence / Foundation Models
- **Target Customers**: B2B; Enterprise (e.g., automotive, finance, defense, e-commerce, biotech); developers building on-device or edge AI applications.
- **Mission**: To build efficient general-purpose AI at every scale — highly capable, compute-optimized, and ready to run on any device.

## Products & Services
- **Liquid Foundation Models (LFMs)**: A new generation of generative AI models optimized for on-device and edge deployment. They achieve state-of-the-art performance with a smaller memory footprint and more efficient inference than traditional transformers. Models range from 230M to 24B parameters, including specialized variants for vision (VL), multilingual search (Retrievers), and mixture-of-experts (MoE).
- **Liquid Edge AI Platform (LEAP)**: A full-stack SDK for fine-tuning, baking, and deploying LFMs to production. Supports runtimes like llama.cpp, MLX, ONNX, CoreML, SGLang, and vLLM, enabling rapid customization and deployment.
- **Research & White-Box Models**: The company publishes its research on liquid neural networks and state-space models in the open, emphasizing explainable, traceable AI over black-box approaches.

## Market Standing
- **Valuation**: Not publicly disclosed.
- **Key Metric**:
  - **Total Funding**: $293.2M
  - **Annual Revenue**: $34.5K (early-stage; likely pre-revenue or nominal)
- **Notable Investors/Partners**: AMD Ventures (led Series A), OSS Capital, Stephen Pagliuca; partners include Mercedes-Benz (in-car intelligence), Insilico Medicine (drug discovery), and hardware/infrastructure providers.
- **Growth Signals**:
  - Headcount of ~91 employees (as of mid-2026), with 8% monthly growth.
  - Active job postings: 18 (quarterly growth of +63.6%).
  - Rapid international expansion: operates in 12 countries including Japan, Germany, Spain, and France.
  - Recent product launch: LFM2.5 series (June 2026) — next-gen on-device models.
  - Strong talent pipeline: hires from MIT CSAIL, Stanford, Amazon, Meta, and Citadel.

## Competitive Advantages
- **Efficiency-First Architecture**: LFMs are designed from the ground up for compute- and cost-optimized inference, enabling deployment on phones, laptops, cars, and other edge devices where traditional LLMs are too large or slow.
- **White-Box Explainability**: The company prioritizes transparent, inspectable models over black-box systems — a key differentiator for regulated industries.
- **Rapid Customization**: The LEAP SDK allows clients to fine-tune and deploy specialized models in minutes, not weeks.
- **Strong Academic Roots**: Spun out of MIT CSAIL with a founding team of world-class researchers in liquid neural networks and state-space models.

## Strategic Focus
- **On-Device & Edge AI**: Primary growth vector is bringing advanced intelligence to processors outside data centers (automotive, mobile, IoT, defense).
- **Enterprise Partnerships**: Deepening collaborations with Mercedes-Benz (automotive) and Insilico Medicine (biotech) to embed LFMs into real-world products.
- **International Expansion**: Building out teams in Japan, Europe, and beyond to capture global demand for localized, on-device AI.
- **Model Line Expansion**: Continuously releasing new model sizes and modalities (vision, retrieval, MoE) to cover a broader range of use cases.

## Why Work Here
- **Mission-Driven & High-Impact**: Work on frontier AI research and products that directly shape how intelligence is deployed in the physical world.
- **Culture of Ownership**: “Leadership sets the destination, but the route is yours to discover and deliver” — high autonomy and trust, with a focus on building over process.
- **Transparent & Meritocratic**: Decisions are documented and traceable; ideas are valued based on evidence, not hierarchy.
- **Global & Diverse Team**: 91 employees across 12 countries; 30% in technical roles, 9% in research. Offices in Cambridge, MA (HQ) and satellite hubs in Japan and Europe.
- **Perks & Environment**:
  - Hybrid/office policy (Cambridge HQ with multiple local offices).
  - Strong emphasis on employee wellbeing: “the company prioritizes the needs and wellbeing of its people.”
  - Continuous learning culture: “everyone is expected to continuously fine-tune their skills.”
- **Recent Open Roles (as of mid-2026)**: ML Research Engineer, Applied ML (RecSys, Vision, Post-Training), Distributed Training Engineer, Solutions Architect, Founding Account Executive, Finance Manager.

## Sources
1. [Liquid AI Official Company Page](https://www.liquid.ai/company)
2. [Liquid AI Official Homepage](https://www.liquid.ai/)
3. [Liquid AI Careers Page](https://jobs.ashbyhq.com/liquid-ai)
4. [Liquid AI LinkedIn](https://www.linkedin.com/company/liquid-ai-inc)
5. [Liquid AI About Page](https://www.liquid.ai/company/about)

## Other roles at Liquid AI

- [Member of Technical Staff -  Inference Systems](https://feeny.ai/job/member-of-technical-staff-inference-systems-liquid-ai-boston-n21ddyv32pqs) — Boston, MA
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- [Member of Technical Staff - Applied ML, Japanese Multimodal](https://feeny.ai/job/member-of-technical-staff-applied-ml-japanese-multimodal-liquid-ai-tokyo-hv79bwy6nmkk) — Tokyo, Japan
- [Member of Technical Staff - Embedded ML Engineer (Audio/Omni)](https://feeny.ai/job/member-of-technical-staff-embedded-ml-engineer-audio-omni-liquid-ai-san-9pyn1p2pa7t5) — San Francisco, CA
- [Product Manager](https://feeny.ai/job/product-manager-liquid-ai-san-francisco-jd34b9k8eehq) — San Francisco, CA
- [Member of Recruiting Staff - Technical Recruiter](https://feeny.ai/job/member-of-recruiting-staff-technical-recruiter-liquid-ai-san-francisco-bsjt2w1p7n1d) — San Francisco, CA
- [Solutions Architect](https://feeny.ai/job/solutions-architect-liquid-ai-san-francisco-qbvwa5nr2h91) — San Francisco, CA
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- [Member of Technical Staff - Applied ML, RecSys](https://feeny.ai/job/member-of-technical-staff-applied-ml-recsys-liquid-ai-boston-njmshfbyy3kq) — Boston, MA
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