---
title: 'Member of Technical Staff - ML Research Engineer, Data at Liquid AI'
canonical: 'https://feeny.ai/job/member-of-technical-staff-ml-research-engineer-data-liquid-ai-san-francisco-1m3kmekfjy1p'
type: 'job'
last_seen: '2026-09-08'
---

# Member of Technical Staff - ML Research Engineer, Data at Liquid AI

- **Company:** Liquid AI
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2025-07-29
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/liquid-ai/c7251e1b-d7bf-4d03-8b9e-1382743bef2c

## 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 Data team powers Liquid Foundation Models across pre-training, vision, audio, and emerging modalities. Public data sources are plateauing. Model performance increasingly depends on purpose-built datasets. We need ML-minded engineers who can collect, filter, and synthesize high-quality data at scale.

We treat data as a research problem, not an infrastructure problem. Our engineers run experiments, design ablations, and measure how data decisions move model quality. We will match you to the team where you can grow the fastest and have the most impact: pre-training, post-training RL, vision-language, audio, or multimodal.

While San Francisco and Boston are preferred, we are open to other locations.

## WHAT WE'RE LOOKING FOR

We need someone who:

- Thinks like a researcher, ships like an engineer: We need people who form hypotheses, run experiments, and measure results. Our engineers understand deep-theoretical research, and our researchers ship production systems.
- Learns fast and adapts: We work across modalities that evolve weekly. We need people who pick up new domains quickly and thrive with ambiguity.
- Obsesses over data quality: We believe data quality is non-negotiable. Filtering, deduplication, augmentation, and evaluation are first-class concerns for our team, not afterthoughts.
- Solves problems independently: Our data engineers sit within training groups (pre-training and multimodal). We collaborate closely, but we expect ownership and self-direction.

## THE WORK

- Build and maintain data processing, filtering, and selection pipelines at scale
- Create pipelines for pretraining, midtraining, SFT, and preference optimization datasets
- Design synthetic data generation systems using LLMs, structured prompting, and domain-specific generators
- Design and run evaluations and ablations to measure dataset's impact on model performance
- Monitor public datasets across text, vision, and audio domains
- Collaborate with pre-training, vision, and audio teams on modality-specific data needs

## DESIRED EXPERIENCE

Must-have:

- Strong Python skills with the ability to quickly comprehend problems and translate them into clean, working code
- Solid ML fundamentals: experience training, evaluating, and iterating on models (PyTorch preferred)
- Track record of learning new technical domains quickly
- 3+ years relevant experience with an M.S., or 1+ year with a Ph.D. (5+ years with a B.S.)

Nice-to-have:

- Experience with synthetic data generation, data curation, or ML evaluation (designing evals, benchmarking, measuring data and model quality)
- Experience with LLMs, VLMs, computer vision, or audio data pipelines
- Open-source contributions or publications at NeurIPS, ICML, ICLR, or CVPR

## WHAT SUCCESS LOOKS LIKE (YEAR ONE)

- You own a critical data pipeline end-to-end for one of our modalities
- You have built or improved data systems that measurably moved model performance
- You have identified and integrated at least one external dataset that moved the needle

## WHAT WE OFFER

- Impact at scale: Your pipelines directly determine model quality across all of Liquid's 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
- [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