Senior Machine Learning Engineer

Who you are

• 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
• 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
• If 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 make the biggest impact

What the job involves

• 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
• The first is , 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
• 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
• 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
• 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

Benefits

• Flexible Work Environment
• Health Insurance
• Unlimited PTO
• Equity
• Growth, Training, & Conferences
• Generous Parental Leave