• Strong background in building production-grade, distributed data systems for machine learning, with experience in:
• Orchestration: Slurm, Airflow, or Dagster
• Observability & Reliability: CI/CD, Grafana, Prometheus, etc.
• Infra: Git, Docker, k8s, cloud managed services
• Batch inference (ex: vLLM)
• Performance obsession, especially with large-scale GPU clusters and distributed pipelines
• Expert-level python knowledge and ability to write clean and maintainable code
• Strong algorithmic foundations
• Proficiency with libraries like Polars, Dask, or PySpark
• Experience in building trillion-scale SOTA pretraining datasets
• Experience translating research to production at scale
• Experience with OCR, web crawling, or evals
• PROCESS
• PROCESS
• Technical Interview(s) with one of our Founding Engineers
• Team fit call with the People team
• Final interview with one of our Founding Engineers