Data at Scale: Own the pipelines and storage systems that feed petabyte-scale multimodal datasets into model training.
Sustainable Platforms: Build tooling and systems that are automated and efficient, enabling processing at scale and handling many small heterogeneous datasets.
Data Engineering: Knowledge of Python ETL pipelines and supporting infrastructure, data formats, and storage systems at scale.
ML Data Ops: Experience managing datasets, annotations, and data versioning for model training.
Basic ML Knowledge: Solid grasp of ML fundamentals is essential to collaborate effectively with researchers and make sound data platform decisions.
Agentic Engineering: Skilled at writing high-quality specifications for AI agents, while maintaining effective human review of AI-generated work.
Design, automate, maintain, and optimize Python ETL pipelines (Spark/Ray) for large-scale multimodal data.
Build and maintain data cataloging, lineage, quality tooling, integrity verification, access controls, and lifecycle management systems.
Provide guidance, internal tools, and documentation to colleagues on data best practices.
Serve as a custodian of the company’s datasets, ensuring overall data health, quality, and discoverability.
Implement high-performance, multimodal data pipelines capable of processing petabyte-scale datasets on 10,000s of CPUs and 100s of GPUs.
Evolve data formats, storage, and processing to keep pace with cutting-edge AI advancements, while maintaining backward compatibility.
Scale data infrastructure to handle the next order of magnitude in growth.
At the same time, ensure the data platform flexible to rapidly handle many small heterogeneous datasets and ad hoc analytics queries.
High agency and ownership: proactively picks up new work according to priority, manages their own backlog, and escalates early when priorities are unclear or deadlines are at risk.
Takes responsibility for validating inputs end-to-end: spot-checks data, understands upstream preprocessing, and speaks up when something doesn't add up.
Takes responsibility for ensuring outputs are correct and handed over: actively seeks sign-off from downstream consumers, communicates caveats, and ensures relevant stakeholders are aware of changes and breaking impacts.
Cares about continuously improving pipelines, tooling, and processes so that each iteration makes the next one faster, more reliable, and easier for the team.
Comfortable with rapid, pragmatic solutions when needed, but committed to high-quality, long-term solutions.
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