---
title: 'Member of Technical Staff -  Inference Systems at Liquid AI'
canonical: 'https://feeny.ai/job/member-of-technical-staff-inference-systems-liquid-ai-boston-n21ddyv32pqs'
type: 'job'
last_seen: '2026-09-15'
---

# Member of Technical Staff -  Inference Systems at Liquid AI

- **Company:** Liquid AI
- **Location:** Boston, MA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-08-26
- **Last confirmed live:** 2026-09-15
- **Apply:** https://jobs.ashbyhq.com/liquid-ai/ebadd261-6908-43fd-bd96-d6259b6a6c31

## Job description

Liquid AI Job Description

Role: Member Of Technical Staff, Infrastructure Department: Research & Engineering Location: Boston Location Type: Hybrid Employment Type: Full-time

## 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 inference stack is central to everything we ship. You'll be a core part of the team responsible for the engine layer that runs our models in production and in partner environments, and for the benchmarking infrastructure we use to evaluate our own work and verify what partners bring to us. Day to day, that means working closely with research and product, but also directly with external engineering teams.

## What We're Looking For

We need someone who:

- Can pick up unfamiliar tools quickly and knows how to assess whether they're worth using.
- Designs AI benchmarks and holds methodology to a high standard.
- Cares about inference details, understands the tradeoffs, and checks what changed across the board before calling something done.
- Doesn’t consider a model port finished until you can prove the outputs are correct.

The Work

- Design and build benchmark suites that cover inference performance, model quality, and knowledge evaluation across different hardware targets.
- Run external partner verifications: evaluate their solutions against our benchmarks, identify gaps, and clearly deliver findings.
- Port models like LFM2 onto different runtimes and frameworks, and verify correctness end-to-end.
- Maintain and extend the inference engine layer built on llama.cpp, ONNX, and MLX as new model architectures emerge from research.
- Make benchmark results explainable and verifiable, so internal teams and partners can trust and reproduce them independently.

Desired Experience Must-have:

- Hands-on experience with at least one inference framework like llama.cpp, ONNX Runtime, or MLX, going beyond basic usage into internals and modification.
- Experience designing and building benchmarking pipelines, including methodology, validation, and reproducibility.
- Strong C++ and Python in performance-sensitive contexts.
- Solid understanding of inference fundamentals: quantization, decoding strategies, memory layout, and how they interact.

Nice-to-have:

- Experience porting models across runtimes and verifying numerical correctness.
- Prior work with external partners or clients in a technical validation or evaluation capacity.
- Familiarity with edge inference targets and the constraints that come with them.

What Success Looks Like (Year One)

- You've ported LFM2 onto multiple runtimes and platforms, you know the model inside out, and new ports take you a fraction of the time they did at the start.
- You've run multiple partner verifications end-to-end and built enough context to spot weak evaluations quickly and push back with evidence.
- The benchmark suite covers inference performance and model quality across the platforms we care about, and both internal teams and partners are using it as a reference.

## What We Offer

- 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

- [Associate General Counsel, Product & Regulatory](https://feeny.ai/job/associate-general-counsel-product-regulatory-liquid-ai-north-america-l-san-cv1mmvgze9a6) — North America L San Francisco CA OR Cambridge, MA
- [Member of Technical Staff - ML Scientist, Japanese Multimodal](https://feeny.ai/job/member-of-technical-staff-ml-scientist-japanese-multimodal-liquid-ai-tokyo-b1sxex046dg8) — Tokyo, Japan
- [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 - GPU Infrastructure Engineer](https://feeny.ai/job/member-of-technical-staff-gpu-infrastructure-engineer-liquid-ai-san-francisco-5qry0efpcqvv) — San Francisco, CA
- [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
- [Member of Technical Staff - Post Training, Applied (Vision)](https://feeny.ai/job/member-of-technical-staff-post-training-applied-vision-liquid-ai-san-francisco-vtjy7ncbazk1) — San Francisco, CA
- [Member of Technical Staff - Applied ML, RecSys](https://feeny.ai/job/member-of-technical-staff-applied-ml-recsys-liquid-ai-boston-njmshfbyy3kq) — Boston, MA