Staff Software Engineer - AI Research Infrastructure
Builds and operates research infrastructure for large-scale AI model training and inference across GPU fleets. Partners with scientists and engineers to create scheduling, orchestration, and dev tooling for efficient experimentation. Requires 5+ years in distributed systems and systems programming.
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)
- Enable researchers to go from idea to large-scale experiment in minutes by building powerful abstractions for job submission, scheduling, and monitoring
- 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 train, evaluate, and ship models to customers
- 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 with 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 (C++, Rust, Go, Java, Scala) and can design, implement, and debug complex services
- Built or significantly contributed to cluster schedulers, resource managers, or large-scale job orchestration systems (Kubernetes, Slurm, Ray, custom internal systems)
- Understand modern ML training and inference workflows (e.g., distributed training, model parallelism, fine-tuning, evaluation)
- Can move fast and be pragmatic while caring about operational excellence; driven complex systems from prototype to stable services
- Communicate clearly with researchers and engineers
Skills
Kubernetes, Slurm, Ray, C++, Rust, Go, Java, Scala, Distributed Systems, Gpus, Cloud Providers, Ml Training, Model Parallelism, Hpc Clusters
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