• Steward the end-to-end planning, execution, and delivery of the data and training infrastructure for the organization. • Act as the primary point of contact for the team that serves infrastructure to multiple other machine learning teams. • Own the cloud spend and implement cost-tracking, resource allocation and lifecycle management. • Service mindset in making the day-to-day of ML engineers smooth, balancing engineering rigor with ease of use.

• Implement core deep-learning, computer vision, and (inverse-)procedural modeling algorithms in Python • Apply cutting-edge research in machine learning and computer graphics to solve real-world problems • Work closely with cofounders to understand high-level product vision and translate customer requirements into technical milestones • Interact with remote machines via a Unix shell to deploy and test code on large-scale geospatial datasets • Use Git to manage source code and modularize complex implementation tasks into manageable, executable components

• Develop and own the backbone of our machine learning lifecycle, ensuring that data pipelines are automated, reproducible, and highly performant at scale • Work on enabling seamless model training, deployment, and monitoring across complex, multimodal systems, supporting the evolution of cutting-edge AI/ML applications • Collaborate with partner ML and Annotation engineers and TPMs to spec out infrastructure and training requirements • Design and maintain robust CI/CD and CT (Continuous Training) pipelines for complex multimodal models • Implement versioning and storage strategies for massive 2D/3D datasets to ensure reproducibility and high-throughput access • Deploy and manage systems for monitoring model performance and data drift in production environments

• Implement core deep-learning, computer vision, and (inverse-)procedural modeling algorithms in Python • Apply cutting-edge research in machine learning and computer graphics to solve real-world problems • Work closely with cofounders to translate customer requirements into technical milestones • Deploy and test code on large-scale geospatial datasets, generating 3D content for customers. • Use Git to manage source code and modularize implementation tasks into executable components.