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Member of Technical Staff - Multi-Modal, Audio

Liquid AI · San Francisco · Boston

CHECKED 22H AGO

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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
More about Liquid AI →
Apply on the lab’s site ↗