Cloud ML DevRel Engineer - US remote

Hugging Face•New York, NY

2d•Remote

About The Position

As a Cloud ML DevRel Engineer at Hugging Face, your primary goal is to expand the influence of the ML Cloud team by educating the ML practitioner community on how to accelerate their training and inference workloads. The ML Cloud team collaborates with major cloud providers (AWS, GCP, Azure, Cloudflare), AI accelerator companies (NVIDIA, AMD, Intel Gaudi, AWS Inferentia, TPU), and systems partners (Dell, Nutanix) to simplify the process for the community to run Hugging Face models and libraries on these platforms. These partnerships are crucial to Hugging Face's strategy as an open platform without customer lock-in, and they drive usage and revenue for partners. This role is fundamentally an engineering position with a significant focus on education and community engagement. Your contributions will drive visibility and adoption of partner integrations through activities such as publishing technical blog posts, contributing to documentation and code examples, presenting to business and technical audiences at conferences, conducting webinars, developing and showcasing demos, and leading go-to-market discussions with strategic partners. You will operate at the forefront of generative AI and open source, collaborating with leading companies in the field. The role offers substantial autonomy and creative freedom, aiming for a significantly greater impact than a similar role in a large tech corporation.

Requirements

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: 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 Haves

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)