Cohere
Software Engineer, Data Infrastructure
About this role
Cohere seeks a Software Engineer to design and optimize petabyte-scale data infrastructure supporting world-class AI model training. You'll work on high-performance storage systems, networking challenges, and data pipelines alongside leading researchers and engineers.
What you'll do
- Build and maintain petabyte-scale storage infrastructure for demanding AI training workloads
- Optimize networking and performance across distributed data systems
- Collaborate with modeling teams on data storage and retrieval for training and evaluation
- Transform unstructured data into performant datasets across multiple storage backends
- Design and implement data pipelines using distributed processing frameworks
- Support researchers and engineers with infrastructure for cutting-edge AI development
What they're looking for
- Data storage infrastructure (4+ years)
- Python
- Kubernetes and storage management (Persistent Volumes, CSI drivers)
- Multi-cloud storage (S3, GCS, POSIX)
- Distributed data processing (Spark, Beam, Flink)
- Analytics tooling (BigQuery, Airflow, dbt preferred)
- AI research knowledge and passion
- Ability to work on novel, unproven systems
Benefits
- Weekly lunch stipend ($75/£75 equivalent)
- Full health, dental, and mental health benefits
- Retirement matching (RRSP, 401K, Pension)
- 100% parental leave top-up for 6 months
- 6 weeks paid vacation (30 working days)
- Learning stipend, enrichment benefits, and $500 home office credit
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Cohere
Cohere builds enterprise AI systems and large language model infrastructure, including audio inference optimization, agentic AI workflows, and petabyte-scale data infrastructure for model training. The company is hiring software engineers, infrastructure specialists, forward-deployed engineers to work with enterprise customers, and IT support staff.
- Website
- cohere.com
Likely interview questions
- Walk us through a project where you designed or optimized petabyte-scale storage infrastructure. What were the key performance bottlenecks and how did you address them?
- Describe your experience with Kubernetes storage, particularly Persistent Volumes and CSI drivers. What challenges have you encountered and how did you solve them?