• Own high quality solutions from design to production, including testing, monitoring, and documentation, while helping shape best practices across the engineering lifecycle • Proactively tackle ambiguous problems, reducing their complexity into clear, simple, and actionable solutions with stakeholder alignment • Ensure all solutions meet the latest security, privacy, and regulatory standards • Drive architectural improvements to scale the platform, evolve our infrastructure, and support new product capabilities. • Collaborate effectively in a globally distributed, diverse, and cross-functional team, adapting as Oura evolves and new opportunities emerge.

• Participate and lead efforts around the development of our flagship DVC product and ecosystem - including leading and owning cross product features and efforts and strong involvement with adjacent projects and products. • We expect strong SW engineering skills and knowledge and excellent coding culture (CQ standards, automation, testing, FOSS contributions, etc). Specifically Strong Python experience is required, as well as and experience building dev-tools and maintaining open source software.

• Analizar, diseñar, desarrollar y mantener proyectos backends y APIs. • Conectar, y procesar fuentes de datos diversas: bases de datos, APIs, ficheros, etc. • Desarrollar scripts o funciones en Python y/o AWS lambdas para tratamiento de datos. • Participar en la definición de soluciones técnicas junto con el equipo técnico y negocio.

• Analysis of new models from generative AI field and understanding of impacts on compilation stack • Develop and maintain model definition framework that consists of model building blocks to represent large language models based on PyTorch and Cerebras dialects ready to be deployed on Cerebras hardware. • Develop and maintain the frontend compiler infrastructure that ingests PyTorch models and produces an intermediate representation (IR). • Extend and optimize PyTorch FX / TorchScript / TorchDynamo-based tooling for graph capture, transformation, and analysis. • Collaboration with other teams throughout feature implementation • Research on new methods for model optimization to improve Cerebras inference