Machine Learning Engineer (Voice) (m/f/d)

Speech-to-text and text-to-speech for real phone calls: noisy lines, dialects, names, street names and dictated numbers

Select open STT and TTS models, fine-tune them on our domain with PyTorch and measure them properly against the baseline

Serve models in production on our own GPUs in the EU: streaming, batching, quantisation, latency budgets and cost per call

Build the voice agent's orchestration layer: turn-taking, barge-in, tool calls and the hand-over to our LLM agent (Python, FastAPI, pydantic-ai)

Evals that gate releases: word error rate on anonymised calls, pronunciation and naturalness tests, end-to-end conversation evals — wired into CI

Monitor quality in production and close the loop: from a failed call to a test case and to better training data

We welcome experienced candidates with several years of work on ML systems just as much as career starters who have mostly worked in notebooks so far

Good PyTorch skills, ideally with hands-on experience fine-tuning speech or audio models

Ideally experience with STT and/or TTS, e.g. Whisper-family models or modern neural TTS

Experience with serving (GPU inference, streaming, quantisation, latency under load) is a plus — otherwise you will learn it with us

A strong quality mindset: you don't ship without an eval, you distrust single examples, and you build benchmarks that predict real-world quality

Solid Python engineering: types, tests, clean interfaces

You use AI coding assistants every day and move fast without putting production at risk

Nice-to-have: real-time voice (telephony/SIP, VAD, barge-in), privacy-preserving ML, German language skills

Competitive salary

Real ownership: your models talk to citizens on live phone lines within your first month

Our own GPU infrastructure in the EU and room to experiment

A beautiful and central office right at Rosenthaler Platz

28 days of vacation per year