• Design, build, and maintain machine learning model productionization infrastructure. These models will drive very visible product features at The Athletic, and your infrastructure is key to their success. • Work with the rest of the data science team to streamline model training, validation, and deployment. • Implement robust monitoring and alerting for model performance, drift, and data quality, ensuring our models remain accurate in production. • Cultivate an outstanding data science environment by championing best practices in ML Ops and evaluating and integrating new technologies into our data science stack.

• educate the community of ML practitioners on accelerating training and inference workloads • publish technical blog posts • contribute documentation and code examples • speak at partner conferences • participate in or produce webinars • build and evangelize demos • lead GTM conversations with strategic partners

• Help build machine learning solutions used by millions leveraging cloud technologies • Work on integrating Hugging Face's open-source libraries with major cloud platforms or managed SaaS solutions • Bridging and integrating transformers/diffusers models with a different Cloud provider • Ensure models meet the expected performance • Design & Develop easy-to-use, secure, and robust Developer Experiences & APIs • Write technical documentation, examples and notebooks to demonstrate new features • Share & Advocate your work and results with the community

• Define and communicate the long-term vision for ML/AI applications and engineering, aligning with corporate strategy. • Partner with senior executives, product, and engineering leaders to prioritize initiatives and allocate resources effectively. • Oversee design, development and deployment of enterprise-scale ML/AI solutions, inference pipelines, and respective technical roadmaps. • Ensure operational excellence in ML/LLM Ops, including automation, observability, and lifecycle management in partnership with the AIML platform engineering team. • Champion adoption of cutting-edge techniques (Deep learning, recommendation engine architecture, graph neural networks, causal inference, LLMs, RLs, etc). Drive responsible AI practices, model interpretability, and compliance with regulatory requirements. • Build and mentor a high-performing organization of managers and senior engineers. Foster a culture of continuous learning, experimentation, and engineering craftsmanship. • Establish standards for security, scalability, and architectural integrity across ML/AI systems. Implement robust governance for data privacy, ethical AI, and risk mitigation.