About the Role

As a Cloud ML DevRel Engineer, your goal is to grow the impact of the Hugging Face ML Cloud team by teaching the community of ML practitioners how to accelerate their training and inference workloads.

The ML Cloud team works through strategic collaborations with the most widely used clouds (AWS, GCP, Azure, Cloudflare), AI accelerators (NVIDIA, AMD, Intel Gaudi, AWS Inferentia, TPU), and systems partners (Dell, Nutanix), to make it easy for the community to run Hugging Face models and libraries on these platforms. These partnerships sit at the core of our strategy as an open platform with no customer lock-in, and of how we drive usage and revenue for our partners.

This is a solid engineering role with a strong flavor of education and community. Your impact comes from driving visibility and usage of partner integrations, through work like:

• Publishing technical blog posts
• Contributing documentation and code examples
• Speaking to business and technical audiences at partner conferences
• Producing and running webinars
• Building and showing off demos
• Leading go-to-market conversations with strategic partners

You'll work at the front edge of generative AI and open source, hand in hand with some of the most important companies in the field. You'll have a lot of autonomy and full creative control, with the goal of having 10x the impact of a similar role at a big tech company.
About You

You're already an active voice in the ML community. You publish, you teach, and people follow your work on LinkedIn and X.

You care about ML engineering, building practical AI applications, shipping them to production, and squeezing the most out of the cloud to accelerate them. You like learning hard engineering concepts and talking them through with other engineers, and you take pride in code that's easy to read. You're a strong communicator and educator, and you enjoy engaging with the ML community in a positive, helpful way.
What you'll need

• 3+ years in developer relations or developer advocacy, specifically for ML or AI products, tools, or platforms
• An established public presence as a technical voice, with a track record of regularly publishing ML/AI content and a demonstrable, engaged audience on LinkedIn and X (Twitter)
• A portfolio of developer-facing content you can point to: technical blog posts, conference talks, demos, code examples, or documentation
• Comfort and experience with public speaking to technical audiences (conferences, webinars, workshops)
• 3+ years of hands-on ML or software engineering experience, including taking models to production
• Experience training or deploying ML models on at least one major cloud (AWS, GCP, or Azure)
• Proficiency in Python
• Practical experience with the Hugging Face stack (Transformers, the Hub, Inference Endpoints) or comparable open-source ML libraries
• Working knowledge of GPUs or AI accelerators (NVIDIA, AMD, Intel Gaudi, AWS Inferentia, or TPU) and of training and inference optimization
• Fluent written and spoken English

Nice to have

• Open-source maintainer or contributor experience
• An active presence in other developer communities (GitHub, Reddit, YouTube, Discord)
• Familiarity with containers and orchestration (Docker, Kubernetes)
• Experience with distributed training or inference-serving frameworks (for example vLLM, TGI, or Ray)

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.