Liquid AI
Member of Technical Staff - ML Research Engineer; Multi-Modal - Audio
Liquid AI, San Francisco, California, United States, 94199
Member of Technical Staff - ML Research Engineer; Multi-Modal - Audio – Liquid AI
We’re building efficient on‑device and edge AI systems that run under real‑time constraints. Your work will shape the next generation of multimodal foundation models used in speech, language, and vision applications across devices and servers. This Role Is For You
You have experience with machine learning at scale. You have worked with audio models and understand the effects of architecture choices on runtime, latency, and quality. 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. audio, text, image, video). 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 have 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 multimodal language models, or specialized audio models (ASR, TTS, voice conversion, vocoders, diarization). 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 audio encoders or decoders or integrated them into language pretraining pipelines with autoregressive or generative objectives. You have experience working with large‑scale audio datasets, understand the unique challenges they pose, and can manage massive datasets effectively. You have strong programming skills in Python, with an emphasis on writing clean, maintainable, and scalable code. What You’ll Actually Do
Invent and prototype new model architectures that optimize inference speed, including on edge devices. 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 audio 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 Speech 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 AI
Spun out of MIT CSAIL, Liquid AI is 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 run where others stall: on CPUs with low latency, minimal memory, and maximum reliability. We’re partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services and are just getting started.
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We’re building efficient on‑device and edge AI systems that run under real‑time constraints. Your work will shape the next generation of multimodal foundation models used in speech, language, and vision applications across devices and servers. This Role Is For You
You have experience with machine learning at scale. You have worked with audio models and understand the effects of architecture choices on runtime, latency, and quality. 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. audio, text, image, video). 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 have 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 multimodal language models, or specialized audio models (ASR, TTS, voice conversion, vocoders, diarization). 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 audio encoders or decoders or integrated them into language pretraining pipelines with autoregressive or generative objectives. You have experience working with large‑scale audio datasets, understand the unique challenges they pose, and can manage massive datasets effectively. You have strong programming skills in Python, with an emphasis on writing clean, maintainable, and scalable code. What You’ll Actually Do
Invent and prototype new model architectures that optimize inference speed, including on edge devices. 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 audio 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 Speech 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 AI
Spun out of MIT CSAIL, Liquid AI is 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 run where others stall: on CPUs with low latency, minimal memory, and maximum reliability. We’re partnering with global enterprises across consumer electronics, automotive, life sciences, and financial services and are just getting started.
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