Member of Technical Staff - Machine Learning Research Engineer; Multi-Modal - Vi

Liquid AI, an MIT spin-off, is a foundation model company headquartered in Boston, Massachusetts. Our mission is to build capable and efficient general-purpose AI systems at every scale.

Our goal at Liquid is to build the most capable AI systems to solve problems at every scale, such that users can build, access, and control their AI solutions. This is to ensure that AI will get meaningfully, reliably and efficiently integrated at all enterprises. Long term, Liquid will create and deploy frontier-AI-powered solutions that are available to everyone.

We're looking for a

Research Engineer / Scientist

with a deep focus on

Vision Language Models

to join our

Multimodal Foundation Model Training

team. You will be at the heart of our efforts to train next-generation multimodal systems by driving innovation in model design, data processing, and large-scale training strategies for vision and vision-language tasks.

This is a highly technical role that combines cutting-edge machine learning research with systems-level thinking. You'll work across the entire model lifecycle-from architecture design to dataset curation to training-and contribute to pushing the frontier of what

Vision Language Models

can achieve.

You're a Great Fit If

You have experience with

machine learning at scale . You're proficient in

PyTorch , and familiar with distributed training frameworks like

DeepSpeed ,

FSDP , or

Megatron-LM . You've worked with

multimodal data

(e.g., image-text, video, visual documents, audio). You've contributed to

research papers ,

open-source projects , or

production-grade multimodal model systems . You understand how

data quality, augmentations, and preprocessing pipelines

can significantly impact model performance-and you've built tooling to support that. You enjoy working in

interdisciplinary teams

across research, systems, and infrastructure, and can translate ideas into high-impact implementations. What Sets You Apart

You've

designed and trained Vision Language Models . You care deeply about

empirical performance , and know how to design, run, and debug large-scale training experiments on distributed GPU clusters. You've developed

vision encoders

or integrated them into language pretraining pipelines with

autoregressive

or

generative objectives . You have experience working with

large-scale video or document datasets , understand the unique challenges they pose, and can manage massive datasets effectively. You've built tools for

data deduplication ,

image-text alignment , or

vision tokenizer development . Some of the Areas You'll Get To Work On

Investigate and prototype new model architectures that

optimize inference speed , including on

edge devices . Lead or contribute to

ablation studies and benchmark evaluations

that inform architecture and data decisions. Build and maintain

evaluation suites

for multimodal performance across a range of public and internal tasks. Collaborate with the

data and infrastructure teams

to build scalable pipelines for ingesting and preprocessing large

vision-language datasets . Work with the

infrastructure team

to optimize model training across large-scale

GPU clusters . Contribute to

publications ,

internal research documents , and

thought leadership

within the team and the broader ML community. Collaborate with the

applied research and business teams

on

client-specific use cases . What You'll Gain

A front-row seat in building some of the most capable

Vision Language Models . Access to

world-class infrastructure , a fast-moving

research team , and deep collaboration across

ML ,

systems , and

product . The opportunity to shape

multimodal foundation model research

with both

scientific rigor

and

real-world impact .