At Liquid, we’re not just building AI models—we’re redefining the architecture of intelligence itself. Spun out of MIT, our mission is to build efficient AI systems at every scale. Our Liquid Foundation Models (LFMs) operate where others can’t: on-device, at the edge, under real-time constraints. We’re not iterating on old ideas—we’re architecting what comes next.
We believe great talent powers great technology. The Liquid team is a community of world-class engineers, researchers, and builders creating the next generation of AI. Whether you're helping shape model architectures, scaling our dev platforms, or enabling enterprise deployments—your work will directly shape the frontier of intelligent systems.
This Role Is For You 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
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
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
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
Spun out of MIT CSAIL, we’re a foundation model company headquartered in Boston. Our mission is to build capable and efficient general-purpose AI systems at every scale—from phones and vehicles to enterprise servers and embedded chips. Our models are designed to run where others stall: on CPUs, with low latency, minimal memory, and maximum reliability. We’re already partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services. And we’re just getting started.
Top Skills
What We Do
Our mission is to build capable and efficient general-purpose AI systems at every scale
