• 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 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 multimodal language models, or specialized audio models (e.g. 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, 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.