高级机器学习工程师，语音代理（EMEA）
Senior Machine Learning Engineer, Voice Agents (EMEA)
在 Hugging Face，我们正在致力于让优质的 AI 更加普及。我们打造了增长最快的 AI 开发者平台，拥有超过 1100 万用户，他们共同分享了超过 300 万个模型、100 万个数据集和 147 万个应用。我们的开源库在 GitHub 上获得了超过 60 万个星标。
**职位简介**
我们正在为 Hugging Face 构建开放的语音代理栈，现寻找一位高级工程师来负责其中的大部分内容。这个职位的两个核心部分是：第一，[speech-to-speech](https://github.com/huggingface/speech-to-speech)，我们用于实时语音代理的开源库；第二，hf-voice，一款新产品，将允许任何开发者通过他们的 Hugging Face 账户构建和部署语音代理。
该库已经为 Reachy Mini 系列机器人提供支持，并且在 Spaces 上有一个[公开演示](https://huggingface.co/spaces/smolagents/hf-realtime-voice)，因此你不会从零开始。但几乎所有关于如何将其变成开发者依赖的产品的细节仍然开放，你将直接参与决策。
**你的任务：**
**- 负责开源库：**
- 对 speech-to-speech 的大部分架构负责：管道设计、延迟预算以及实时循环的可靠性。
- 集成新出现的 ASR、TTS 和端到端语音模型，同时保持抽象层简洁，适应不断变化的模型生态。
- 审核社区 PR，分类问题，发布版本，并扩大项目贡献者群体。
**- 推出 hf-voice：**
- 设计开发者 API 和流协议：会话生命周期、传输（WebSockets/WebRTC）、认证、错误语义、版本控制。
- 构建服务端：GPU 实时推理、并发、自动扩展、可观测性以及每会话成本。
- 与 Hub 和推理团队合作，使语音代理易于集成到产品和演示中。
- 将产品从演示阶段推向生产环境：负载测试、SLO、当模型或网络路径出现问题时的优雅降级。
**- 在开源环境中工作：**
- 编写文档、示例和模板，让开发者在几分钟内就能运行一个代理。
- 支持已经依赖该栈的部署，从 Reachy Mini 系列机器人开始。
- 如果你喜欢，可以公开谈论这项工作：撰写博客文章、进行演示、发表会议演讲。我们承担差旅和准备时间的费用。
**我们寻找的人：**
- 高级工程师，能够负责架构的较大一部分并推动其发展。
查看英文原文
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 Role**
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 [speech-to-speech](https://github.com/huggingface/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](https://huggingface.co/spaces/smolagents/hf-realtime-voice) 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.
**What 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 You**
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.
**One more thing**
At Hugging Face we believe great AI shouldn't require a massive cluster, we build for everyone, especially the GPU-poor. And because we read every application, here's a small sign that you read this one too: start your answer to the first application question with the words “ **GPU-poor and proud** 🤗”. No trick, no catch, it just tells us a real person is on the other side.
**More about Hugging Face**
**We are actively working to build a culture that values diversity, equity, and inclusivity**. We are intentionally building a workplace where people feel respected and supported—regardless of who you are or where you come from. We believe this is foundational to building a great company and community. Hugging Face is an equal opportunity employer and we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.
**We value development.** You will work with some of the smartest people in our industry. We are an organization that has a bias for impact and is always challenging ourselves to continuously grow. We provide all employees with reimbursement for relevant conferences, training, and education.
**We care about your well-being.** We offer flexible working hours and remote options. We offer health, dental, and vision benefits for employees and their dependents. We also offer parental leave and flexible paid time off.
**We support our employees wherever they are**. While we have office spaces in NYC and Paris, we're very distributed and all remote employees have the opportunity to visit our offices. If needed, we'll also outfit your workstation to ensure you succeed.
**We want our teammates to be shareholders**. All employees have company equity as part of their compensation package. If we succeed in becoming a category-defining platform in machine learning and artificial intelligence, everyone enjoys the upside.
**We support the community**. We believe major scientific advancements are the result of collaboration across the field. Join a community supporting the ML/AI community.