Voice AI Engineer

About this task

The problem
Off-the-shelf speech models are trained for clean English. Our users speak dozens of African and Middle Eastern dialects, over noisy lines, code-switching mid-sentence. The gap between a model that works in the demo and one that works for them is the whole job. You'll own the fine-tuning that closes it: taking base speech and language models and making them actually understand, and sound natural to, the people we serve.
Why it's hard here
This is low-resource, messy-data territory. For a lot of the dialects we care about, the clean training corpus you'd want doesn't exist, so you'll be building it: sourcing, cleaning, labeling, and augmenting audio, then running the fine-tunes and proving they moved the numbers that matter. Word error rate on a real accent, not a benchmark. Naturalness a native speaker would actually sign off on. You'll live in the loop between data, training runs, and evaluation.
What You'll Own
Training/ fine-tuning STT and TTS (and where relevant, the language layer) for our target dialects and acoustic conditions
The training data pipeline: sourcing, cleaning, labeling, and augmentation for low-resource languages
Evaluation you can trust: WER on real-world audio, naturalness, robustness to noise and code-switching
Feeding real production performance back into the next round of training
What We're Looking For
Strong applied ML experience, with models you've fine-tuned and shipped to production
Hands-on with speech: ASR/STT and/or TTS, and the training stack around them (PyTorch, etc.)
Real experience with the unglamorous parts: data collection, cleaning, and labeling for messy audio
Rigor around evaluation, especially where standard benchmarks don't reflect your users
Comfort owning a model end to end and shipping fast with imperfect data
Nice to have
Low-resource, multilingual, or multi-dialect speech work
Experience with modern speech architectures (Whisper-family, speech-to-speech, neural TTS)
A feel for the latency and size constraints of serving these models in real time
Genuine interest in emerging markets or speech tech
The setup
You'll work directly with our CTO on a team of ~10 that ships constantly. High ownership, short path to decisions, real users the day you ship. London-based, in-office.