Work With UsAt 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
Desired Experience:- 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
What You'll Actually Do:- 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
About Liquid AISpun 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.
