Machine Learning Engineer - Speech Model Training

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 fastStrong 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