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.
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 t

📌 AI Engineer (España)
🏢 AethexAI
📍 España