Nous Research - Data Engineer

• Build, operate, and continuously improve the data pipelines that feed analytics and model training. • Deliver reliable internal analytics so teams across Nous can make decisions on their own data. • Wrangle long-horizon agentic data into structured, well-documented datasets ready for analysis and training. • Choose ETL/ELT approaches based on destination capabilities, reproducibility, latency, and cost, and explain the tradeoffs behind each choice. • Own pipeline operations in production, including failures, re-runs, and backfills, and debug issues across jobs, storage, permissions, and downstream consumers. • Design and evolve schemas, storage formats, and data models as needs change. • Build memory-efficient processing that scales, using streaming and partitioning, and recognize when a workload has outgrown a single machine. • Communicate clearly about data location, lineage, quality, limitations, and fitness for use to engineers and non-engineers alike. • Strong Python, plus solid shell skills for batch and storage operations. • Hands-on cloud experience with object storage, batch compute, and identity and access management. • Production experience with at least one workflow orchestration system such as Airflow, Dagster, Prefect, or an in-house equivalent. • An instinct for data quality: you ask "is the data good?" and not just "did the pipeline run?" • Extensive use of AI-assisted development tools, with disciplined review of what those tools produce. • Experience working with agentic or LLM-generated data, including structured, multi-step, tool-conditioned interactions. • Experience building data pipelines for model training. • Experience processing datasets at the scale of millions of interactions or more. • Background at data infrastructure, observability, or AI data companies. • Email [email protected] with the following information: • The role you are applying for in the subject • Portfolio/GitHub showcasing your work