Causal

Causal

Member of Technical Staff — Data Infrastructure

San Francisco · Staff+

Sponsorship not specifiedDetected 3 days ago
Cloud PlatformsSparkData EngineeringRoboticsResearchProblem Solving

About the role

  • Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.
  • We look for data engineers who are excited to tackle unsolved problems.
  • Physical observations arrive continuously, in many formats, at a scale that dwarfs what is used to train today's LLMs.

Responsibilities

  • Design and operate petabyte-scale storage: lakehouse architecture, file formats, and data layout optimized for both batch and real-time queries
  • Own the shared compute and orchestration platform (e.g. Spark, Ray, workflow scheduling) that ingestion and research pipelines run on
  • Optimize data strategy end to end from storage to loading, owning high-throughput data loading into training up to the tensor boundary
  • Build systems for cataloging, deduplication, lineage, search, and reproducibility at every stage of the data lifecycle
  • Implement the platform-level quality and monitoring tooling that data and research teams build their checks on
  • Work across the full data lifecycle when the mission needs it - including building and operating ingestion pipelines for critical data sources directly
  • Demonstrated experience building large-scale data pipelines and distributed compute systems (e.g. Spark, Ray, Beam)
  • Owns deliverables end-to-end, from collecting and translating requirements to autonomously driving execution

Requirements

  • We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.
  • Knowledge of state-of-the-art methods and tools for data ingestion, storage, and loading - including file formats and storage systems (e.g. Parquet, Zarr, Delta Lake) and how they impact performance and scalability
  • We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Company info

  • What we're looking for
Apply directly at Causal →Create a free account for alerts like thisView Causal immigration profile

This listing is sourced directly from Causal's careers page and normalized into a canonical job model.