• Design and deliver AI/ML solutions for operational readiness and faster decision-making • Lead development of AI/ML architectures for fleet operations and logistics • Collaborate with cross‑functional teams to translate complex requirements • Drive outcomes such as reduced processing times and increased predictive accuracy • Utilize modern technologies like TensorFlow, PyTorch, and secure computation to meet security requirements

• Design, build, and operate backend services that power context retrieval and enrichment for AI assistants and agents. • Build platform capabilities for storing, searching, and retrieving “insights” and relevant facts across HubSpot’s GTM data. • Develop systems to manage and compress context when it gets large (e.g., long contact histories, high-volume CRM data). • Create tooling that allows other engineering teams to ship assistants/agents faster, with consistent APIs and reusable primitives. • Build and maintain evaluation and measurement approaches (offline evals, golden datasets, automated metrics, human review loops) to ensure context quality and answer accuracy. • Collaborate closely with sister platform teams and downstream product engineering teams (your “customers”) to integrate platform capabilities into real experiences. • Own end-to-end delivery: architecture, implementation, observability, performance, reliability, and iteration.

• Design, build, and operate backend services that power context retrieval and enrichment for AI assistants and agents. • Build platform capabilities for storing, searching, and retrieving “insights” and relevant facts across HubSpot’s GTM data. • Develop systems to manage and compress context when it gets large (e.g., long contact histories, high-volume CRM data). • Create tooling that allows other engineering teams to ship assistants/agents faster, with consistent APIs and reusable primitives. • Build and maintain evaluation and measurement approaches (offline evals, golden datasets, automated metrics, human review loops) to ensure context quality and answer accuracy. • Collaborate closely with sister platform teams and downstream product engineering teams (your “customers”) to integrate platform capabilities into real experiences. • Own end-to-end delivery: architecture, implementation, observability, performance, reliability, and iteration.

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 an open-source Machine Learning Engineer, you will work to improve the open-source machine learning ecosystem. You will mainly work with existing open-source libraries, such as Transformers, Datasets, or Accelerate, and you will interact with users and contributors of the broad open-source machine learning ecosystem. We'll brainstorm with you to put you in a position to do the work that interests you and that is impactful. You'll get to foster one of the most active machine learning communities, helping users contribute to and use the tools that you build. You'll interact with Researchers, ML practitioners and data scientists on a daily basis through GitHub, our forums, or slack. About you If you love open-source, are passionate about making complex technology more accessible, and want to contribute to one of the fastest-growing ML ecosystems, then we can't wait to see your application! 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 background complement one another. We're happy to consider where you might be able to make the biggest impact. 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 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. Requirements Please provide a cover letter mentioning why you would like to work in open-source at Hugging Face. We encourage you to mention your skills, potential expertise, and topics on which you would like to work.