---
title: 'Member of Technical Staff - Post Training, Applied (Vision) at Liquid AI'
canonical: 'https://feeny.ai/job/member-of-technical-staff-post-training-applied-vision-liquid-ai-san-francisco-vtjy7ncbazk1'
type: 'job'
last_seen: '2026-09-15'
---

# Member of Technical Staff - Post Training, Applied (Vision) at Liquid AI

- **Company:** Liquid AI
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-03-30
- **Last confirmed live:** 2026-09-15
- **Apply:** https://jobs.ashbyhq.com/liquid-ai/286613f3-3401-4b54-aa0a-deb498ae79df

## 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 This is a rare chance to sit at the intersection of frontier vision-language models and real-world deployment. You'll own applied post-training work for VLMs end-to-end for some of the world's largest enterprises, while still contributing directly to Liquid's core multimodal model development. Unlike most roles that force a trade-off between customer impact and foundational work, this role gives you both: deep ownership over how vision-language models are adapted, evaluated, and shipped, and a direct line into the evolution of Liquid's multimodal post-training stack. If you care about visual understanding, data quality, evaluation, and making VLMs actually work in production, this is a chance to shape how applied multimodal AI is done at a foundation model company.

## What We're Looking For

We need someone who:

- Takes ownership: Owns VLM post-training projects end-to-end, from customer requirements through delivery and evaluation.
- Thinks end-to-end: Can reason across visual data curation, training, alignment, and evaluation as a single system.
- Is pragmatic: Optimizes for model quality and customer outcomes over publications or theory.
- Communicates clearly: Can translate between customer needs and internal technical teams, and push back when needed.

The Work

- Act as the technical owner for enterprise customer VLM post-training engagements.
- Translate customer requirements into concrete multimodal post-training specifications and workflows.
- Design and execute visual data generation, filtering, and quality assessment processes, including image-text pair curation, annotation pipelines, and synthetic data generation for visual tasks.
- Run supervised fine-tuning, preference alignment, and reinforcement learning workflows for vision-language models.
- Design task-specific evaluations for visual understanding, grounding, OCR, document parsing, and other multimodal capabilities. Interpret results and feed learnings back into core post-training pipelines.

Desired Experience Must-have:

- Hands-on experience with data generation and evaluation for VLM or multimodal post-training.
- Experience training or fine-tuning vision-language models using SFT, preference alignment, and/or RL.
- Strong intuition for visual data quality, annotation design, and multimodal evaluation.
- Familiarity with vision encoders, image-text architectures, and how visual representations interact with language model backbones.

Nice-to-have:

- Experience with visual grounding, document understanding, OCR, or video understanding tasks.
- Experience contributing to shared or general-purpose multimodal post-training infrastructure.
- Prior exposure to customer-facing or applied ML delivery environments.
- Familiarity with alignment or RL techniques beyond basic supervised fine-tuning in the multimodal setting.

What Success Looks Like (Year One)

- Independently owns and delivers enterprise VLM post-training projects with minimal oversight.
- Is trusted by customers as the technical owner, demonstrating strong judgment and delivery quality on multimodal workloads.
- Has made durable contributions to Liquid's general-purpose multimodal post-training pipelines by feeding applied learnings back into baseline model development.

## What We Offer

- Real ML work: You will fine-tune vision-language models, generate multimodal data, and ship solutions, not configure API calls. Your work feeds directly back into our core model development.
- 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 -  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
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- [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 - Applied ML, RecSys](https://feeny.ai/job/member-of-technical-staff-applied-ml-recsys-liquid-ai-boston-njmshfbyy3kq) — Boston, MA