[Hiring] Senior Machine Learning Engineer @Hugging Face

Role Description

We 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:

• Speech-to-speech: our open-source library for realtime voice agents.
• 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.

Qualifications

• 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.

Requirements

• 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.

Benefits

• Flexible working hours and remote options.
• Health, dental, and vision benefits for employees and their dependents.
• Parental leave and flexible paid time off.
• Reimbursement for relevant conferences, training, and education.
• Company equity as part of the compensation package.