Senior Machine Learning Engineer, Voice Agents - EMEA Remote

Stack

Responsibilities

• ▹Take architectural ownership of large parts of the open-source speech-to-speech library: pipeline design, latency budget and reliability of the realtime loop
• ▹Integrate new ASR, TTS and end-to-end speech models and keep abstractions clean
• ▹Review community PRs, triage issues, cut releases and grow the contributor group
• ▹Design the hf-voice developer API and streaming protocol: session lifecycle, transport (WebSockets/WebRTC), authentication, error semantics, versioning
• ▹Build the serving side: realtime GPU inference, concurrency, autoscaling, observability and cost per session
• ▹Work with the Hub and inference teams to make integration easy
• ▹Take the product from demo to production: load testing, SLOs, graceful degradation when a model or network path misbehaves
• ▹Write docs, examples and templates and support existing deployments such as the Reachy Mini fleet
• ▹Talk about the work publicly if you enjoy it: blog posts, demos, conference talks (travel and prep time covered)

Requirements

• ▹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 similar
• ▹Substantial open-source contributions to a Python library; comfortable with async Python and distributed systems, including their failure modes
• ▹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

Nice to 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

Soft skills

What we offer

• ▹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 conferences, training and education
• ▹Workstation outfitting and the chance to visit the NYC and Paris offices
• ▹Company equity as part of compensation

About the company

Hugging Face aims to democratize good AI: it is building the fastest growing platform for AI builders, with over 11 million users who shared over 3M models, 1M datasets and 1.47M apps. Its open-source libraries have more than 600k GitHub stars.