Senior Machine Learning Engineer, Voice Agents - EMEA Remote

At Hugging Face, we're on a journey to democratize good AI. We are building the fastest growing platform for AI builders with over 11 million users who collectively shared over 3M+ models, 1M+ datasets & 1.47M+ apps. Our open-source libraries have more than 600k+ stars on Github.

About the RoleWe are building the open voice-agent stack for Hugging Face, and we are looking for a senior engineer to own a large part of it.Two things sit at the centre of this role. The first is speech-to-speech, our open-source library for realtime voice agents. The second is hf-voice, a new product that will let any developer build and deploy voice agents with their Hugging Face account.

The library already powers the Reachy Mini fleet and there is a public demo running on Spaces, so you won't start from a blank page. But almost everything about how this becomes a product developers rely on is still open, and you will have a direct say in it.

Your missions:- Own the Open-Source Library:

• Take architectural ownership of large parts of speech-to-speech: pipeline design, latency budget, and the reliability of the realtime loop.

• Integrate new ASR, TTS and end-to-end speech models as they land, and keep the abstractions clean while the model landscape keeps moving.

• Review community PRs, triage issues, cut releases, and grow the group of contributors around the project.

• Ship hf-voice:
• Design the developer API and the streaming protocol: session lifecycle, transport (WebSockets/WebRTC), authentication, error semantics, versioning.

• Build the serving side: realtime inference on GPU, concurrency, autoscaling, observability, and cost per session.

• Work with the Hub and inference teams so that a working voice agent is easy to integrate into products and demos.

• Take the product from demo to production: load testing, SLOs, graceful degradation when a model or a network path misbehaves.

• Work in the Open:
• Write the docs, examples and templates that get a developer from zero to a running agent in minutes.

• Support the deployments already relying on the stack, starting with the Reachy Mini fleet.

• Talk about the work publicly if you enjoy it: blog posts, demos, conference talks. We cover the travel and the prep time.

RequirementsWhat we're looking for

• Senior engineer, able to own a substantial part of an architecture and drive it forward autonomously.

• Experience building developer-facing infrastructure at an AI or developer-tools company: inference APIs, agent infrastructure, or something comparable.

• Substantial open-source contributions to a Python library. Comfortable with async Python and distributed systems, including their failure modes.

• You have shipped something realtime: streaming, WebSockets or WebRTC, audio or video pipelines, live inference.

• Practical experience with LLMs or multimodal models in production. Clear written communication and a habit of collaborating async and in public.

• Motivated by voice and conversational AI.

Bonus points if you have

• Contributions to a voice-agent framework such as speech-to-speech, pipecat, LiveKit Agents, Vocode or TEN.

• Contributions to llama.cpp or another low-level inference runtime.

• Hands-on work with ASR, TTS or end-to-end speech models, including evaluation of latency and quality trade-offs.

• GPU serving, quantization, or on-device inference experience.

• Audio pipeline knowledge: VAD, echo cancellation, jitter buffers, barge-in and turn detection.

• Experience shipping to embedded or robotics targets.

• A public track record: talks, blog posts, demos.

About YouIf you're interested in joining us, but don't tick every box above, we still encourage you to apply! We're building a diverse team whose skills, experiences, and backgrounds complement one another. We're happy to consider where you might be able to...