Liquid AI · 3 weeks ago
Member of Technical Staff - ML Research Engineer; Multi-Modal - Audio
San Francisco
Full-time
Hybrid
Mid, Senior LevelLiquid AI, spun out of MIT, is focused on building efficient AI systems at every scale. The ML Research Engineer will work on designing and prototyping new model architectures, collaborating with teams to optimize large-scale audio datasets, and contributing to research publications within the broader ML community.
Artificial Intelligence (AI)Foundational AIGenerative AIInformation TechnologyMachine Learning
Responsibilities
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
Qualification
Required
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
Preferred
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
Company
Liquid AI
Build efficient general-purpose AI at every scale.
Founded in 2023
51-200 employees
H1B Sponsorship
Liquid AI has a track record of offering H1B sponsorships. Please note that this does not
guarantee sponsorship for this specific role. Below presents additional info for your
reference. (Data Powered by US Department of Labor)
Distribution of Different Job Fields Receiving Sponsorship
Represents job field similar to this job
Trends of Total Sponsorships
2025 (2)
Funding
Current Stage
Growth StageTotal Funding
$293.1MKey Investors
AMD VenturesOSS Capital L.P.
2024-12-13Series A· $250M
2023-12-01Seed· $37.5M
2023-05-05Seed· $5.6M
Leadership Team
Ramin Hasani
Co-founder and CEO
Mathias Lechner
Co-founder and CTO
Recent News
2025-12-06
Digital Commerce 360
2025-11-15
Company data provided by crunchbase