Staff Software Engineer - AI Research Infrastructure
Build and operate the large-scale training and inference infrastructure that powers Databricks AI Research, enabling researchers to run experiments across thousands of GPUs. Partner with ML scientists and platform teams to deliver reliable, high-performance orchestration and tooling.
About the job
Responsibilities
- Design and implement infrastructure that supports large‑scale experiments, data processing, and model training (e.g., HPC clusters, GPU fleets, or cloud‑based systems)
- Build powerful abstractions for job submission, scheduling, and monitoring so researchers can go from idea to large‑scale experiment in minutes
- Create tooling that improves research developer productivity, such as experiment management systems, CI/testing infrastructure for research code, and workflows that reduce iteration time
- Influence the long‑term roadmap for research computation, shaping how Databricks AI Research trains, evaluates, and ships models
- Serve as a technical mentor and force multiplier for other engineers working on compute, infra, and AI systems
Requirements
- BS/MS or PhD in Computer Science or related field
- 5+ years of software engineering experience, including substantial time working on large‑scale distributed systems or infrastructure
- Deep experience building and operating distributed systems, data pipelines, or large‑scale backend services, ideally involving GPUs, clusters, or major cloud providers
- Proficient in one or more systems programming languages (e.g., C++, Rust, Go, Java, Scala) and able to design, implement, and debug complex services
- Experience building or significantly contributing to cluster schedulers, resource managers, or large‑scale job orchestration systems (e.g., Kubernetes, Slurm, Ray, custom internal systems)
- Understanding of modern ML training and inference workflows (e.g., distributed training, model parallelism, fine‑tuning, evaluation)
- Ability to move fast and be pragmatic while caring about operational excellence; experience driving complex systems from prototype to stable, well‑owned services
- Strong communication skills with both researchers and engineers
Skills
Kubernetes, Slurm, Ray, C++, Rust, Go, Java, Scala, Gpu Clusters, Distributed Systems
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