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 2M models, 700k datasets & 600k apps. Our open-source libraries have more than 600k+ stars on Github. About the Role As a Cloud Machine Learning Evangelist, your goal will be to increase the impact of the Hugging Face ML Cloud team by educating the community of ML practitioners on how they can benefit by accelerating their training and inference workloads. The Hugging Face ML Cloud team is working through strategic collaborations with the most used Clouds (AWS, GCP, Azure, Cloudflare), AI Accelerators (incl. NVIDIA, AMD, Intel, Gaudi, Inferentia, TPU), and Systems (Dell, Nutanix), to make it easy for the community to use Hugging Face models and libraries on these compute platforms. These partnerships are core to Hugging Face product strategy as an open platform (no lock-in of customers), and monetization strategy to drive usage and revenue for our partners through commercial collaborations and product extensions. This is not a marketing role, or a business development role. Your impact will be driving visibility and usage of integrations with strategic partners, through activities including: - Publishing technical blog posts - Contributing documentation and code examples - Speaking to business and technical audiences at partner conferences, - Participating in, or producing webinars - Building and evangelizing demos - Leading GTM conversations with strategic partners. You will be at the forefront of Generative AI (and how to build practical stuff with open source). You will work hand in hand with the most important companies in AI. You will enjoy a lot of autonomy and full creative control, with the goal to have 10x more impact than a similar role at a big tech corporation. About You You are passionate about ML Engineering, building practical AI applications, putting them in production, and accelerating them to the best of the Cloud ability. You love learning new challenging engineering concepts and technologies, and discussing them with engineers. You appreciate a good Developer Experience, and take pride in your code being easy to understand. You are a great communicator and educator, comfortable (as much as one can be!) with public speaking to technical audiences. You love engaging with the ML community in a positive and helpful way. Existing engagement in social platforms (GitHub, LinkedIn, Twitter, Reddit, etc) or other communication/education channels is expected. Having experience in Open Source development will be helpful.. 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.

• Build and lead a world-class applied research team, hiring and mentoring researchers who excel at the intersection of deep learning theory and production engineering. • Define the research roadmap to support the 'Intelligent Canvas,' identifying opportunities to model complex user behaviors—from multi-user collaboration on an infinite canvas, to multi-format AI-powered generation (e.g. slide deck, technical diagram, web app prototypes, etc.), and more. • Pioneer research on unique spatial datasets. Unlike standard text/image corpuses, you will explore massive multimodal and graph-based datasets, uncovering how teams organize information spatially and collaborate together to solve complex multi-modal problems. • Drive the 'Research-to-Product' velocity. You will create the framework for rapidly testing foundation models (e.g., GPT-4, Llama, Stable Diffusion) and fine-tuning them for specific domain tasks (e.g. prototype generation, diagram generation, mindmap generation). • Cultivate a culture of scientific rigor. Encourage the team to stay at the cutting edge (NeurIPS, CVPR) while maintaining a relentless focus on shipping features that delight users. • Partner with Engineering & Product Leadership to translate abstract AI capabilities into intuitive solutions that feel like magic to our users. • Architect organizational processes for model governance, ensuring rigorous evaluation frameworks, reproducibility, and ethical AI practices.

• Develop solutions for autonomous driving, from experimentation to full commercialization. • Explore new ideas using deep learning, neural networks, and large foundation models. • Work on object detection, tracking, prediction, planning, control, online mapping, and more. • Manage the full life cycle of machine learning projects, including data analysis and model verification. • Collaborate with product, simulation, and algorithm teams to integrate machine learning technologies.

• Build and own end-to-end ML pipelines spanning data, training, evaluation, inference, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety