Job Details

Machine Learning Engineer – Speech Model Training
$250,000 - $300,000
San Francisco, CA
Hybrid, 3x per week in office
Full time / Permanent

In this role you won't be wrapping APIs or fine-tuning existing models. You'll be building models across raw acoustic signal processing all the way through to production inference on edge devices. At a company that actually ships to 1.5M+ live users.

A profitable, fast-growing AI company ($250M ARR in under three years, no VC dependency) is standing up a SpeechLLM lab from scratch. This is a founding seat on that team.

They build a hardware-software AI companion used daily by over 1.5 million professionals worldwide. The next chapter is a world-class speech intelligence core and they need the engineers to architect it.

What you'd own:

• Design and train large-scale speech models end-to-end. Unified SpeechLLMs, ASR, expressive TTS, generative audio
• Own the full stack from acoustic feature engineering to GPU cluster optimisation
• Run and optimise distributed training at scale via PyTorch or JAX, FSDP, DeepSpeed, etc
• Drive real-time inference performance with vLLM, TensorRT-LLM, or SGLang
• Apply RL alignment techniques to improve conversational quality
• Debug the hard problems in distributed infrastructure and ship solutions

You likely have:

• Proven experience training large-scale audio or speech models from the ground up
• Deep PyTorch or JAX expertise with real distributed training experience
• Genuine comfort traversing the entire ML stack from signal processing to production
• A bias toward shipping: you take ownership, you iterate fast

Strong bonus: neural audio codecs, diffusion/flow-matching architectures, or LLM pretraining experience.

Why join

• Profitable company at ~$250M run rate - you'll see the impact of your work immediately in a product used daily by professionals worldwide
• Direct ownership of the live speech quality stack, not a supporting role in a large org
• Hybrid San Francisco team with real access to large, diverse, multilingual audio datasets
• Short feedback loops - improvements ship fast and metrics are visible
• Clear path toward senior technical leadership as the audio team grows