Member of Technical Staff - Multi-Modal, Audio
Liquid AI · San Francisco · Boston
CHECKED 22H AGO
Frontier labremote
At a glance
Highlights
- Build frontier speech-language models for STT, TTS, and speech-to-speech.
- Ship on-device audio systems under real-time latency and memory constraints.
- Own data pipelines, evals, and customer deployments end to end.
What they offer
- Equity in a unicorn-stage company
- 100% medical, dental, and vision premiums for employees and dependents
- 401(k) matching up to 4% of base pay
- Unlimited PTO plus company-wide Refill Days
Ideal candidate
A builder who ships production systems end to end and thrives under on-device constraints. You take ownership from data to deployment and ramp fast on new technical territory.
Requirements
- Strong programming fundamentals with clean, maintainable, production-grade code
- Experience building and shipping production ML systems beyond training
- Proficiency in PyTorch and distributed training frameworks (DeepSpeed, FSDP)
- Track record collaborating in shared codebases with high engineering standards
- Direct experience with audio/speech models (ASR, TTS, vocoders, diarization)
- Experience running large-scale training on distributed GPU clusters
Lab profile
About Liquid AI
MIT CSAIL spinout building efficient general-purpose AI for cloud to edge.
What they build
- Liquid Foundation Models
- LFM2.5-Audio
Known for
- Efficiency-first foundation models
- Data center to on-device deployment
- Low latency with minimal memory usage
- Liquid Neural Networks architecture
Key facts
- Spun out of MIT CSAIL