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.
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
About You
You're already an active voice in the ML community who publishes and teaches. 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 take pride in code that's easy to read and 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
- A portfolio of developer-facing content: technical blog posts, conference talks, demos, code examples, or documentation
- Comfort and experience with public speaking to technical audiences
- 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 and training/inference optimization
- Fluent written and spoken English