• Build and operationalize the infrastructure that allows machine learning to run reliably in production. • Architect and implement Built’s foundational ML Ops platform from scratch • Define and deploy reusable patterns for model training, deployment, monitoring, and retraining • Build CI/CD pipelines for ML lifecycle automation, including versioning and experimentation tracking • Stand up a feature store integrated with Snowflake and AWS to support structured and unstructured data • Implement model registry and governance standards to ensure reproducibility, auditability, and rollback capability • Integrate ML workloads into our event-driven architecture (Kafka, Kinesis) • Develop observability frameworks to monitor drift, performance, latency, and model quality in production • Automate ML infrastructure using Terraform and AWS-native tooling (SageMaker, Lambda, ECS, Batch, Step Functions) • Establish security and compliance standards across ML assets, including data lineage and access control • Mentor engineers on ML Ops patterns and deployment best practices

• Own and manage the backlog for ML-driven biometric and document verification capabilities. • Translate fraud, identity, and customer requirements into clear and actionable ML work items. • Partner closely with ML engineers and data scientists to refine problem statements into feasible deliverables. • Define acceptance criteria that reflect real world performance, not just offline model metrics. • Participate actively in model design discussions, prioritization, and tradeoff analysis. • Support model lifecycle activities including training, evaluation, deployment, and retraining. • Ensure monitoring, drift detection, and feedback loops are incorporated into delivery plans. • Partner with agent operations and data teams on labeling strategy and data quality. • Incorporate fraud patterns and adversarial thinking into backlog prioritization. • Work closely with engineering, fraud, compliance, legal, and customer teams.

• Lead the design and implementation of ML infrastructure • Develop scalable data pipelines for ML models • Optimize data loading and processing systems • Collaborate with research teams to productionize models • Implement deployment strategies across edge devices and cloud platforms • Build automated testing and monitoring pipelines • Mentor team members on MLOps best practices

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.