Cohere
Mid
Software Engineer, Data Infrastructure — Cohere
Cohere is hiring a Software Engineer for its Data Infrastructure team (under the Agentic Platform org) to build the petabyte-scale storage and data layer that Modeling teams rely on for training and evaluation of frontier AI models. The role wants 4+ years on data storage infrastructure, strong Python, Kubernetes storage internals (PVs, CSI drivers), and experience with distributed processing frameworks like Beam/Spark/Flink across S3/GCS/POSIX backends. Expect systems-design depth on storage performance at scale plus genuine engagement with AI research.
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What this interview tests
- Petabyte-scale storage systems design
- Kubernetes storage internals (PV, CSI drivers)
- Distributed data processing (Beam/Spark/Flink)
- Multi-backend data engineering (S3/GCS/POSIX)
- Data pipelines for ML training/evaluation
- Python systems programming
Common question themes
Design a storage layer to serve petabyte-scale training data to hundreds of GPU workers reliably
Explain how you've used or built a Kubernetes CSI driver / managed Persistent Volumes at scale
Walk through transforming unstructured raw data into a performant training dataset across S3/GCS/POSIX
Debugging a throughput or latency bottleneck in a Spark/Beam/Flink pipeline
How is a data pipeline for ML training/eval different from a typical analytics ETL pipeline
Trade-offs between object storage and POSIX-backed storage for training workloads
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