Staff Software Engineer - AI Research Infrastructure
$199k - $270k • Remote • New York City, New York; San Francisco, California
Posted 26d ago
About the job
Databricks is seeking a Staff Software Engineer to join their AI Research Infrastructure team. In this role, you will be responsible for developing and operating the research stack that powers Databricks' AI research efforts. You will design and build services for scheduling, orchestrating, and observing large-scale training and inference experiment workloads across thousands of GPUs. Your work will involve improving developer tooling to ensure researchers can iterate quickly while maintaining reliability, efficiency, and security. You will collaborate closely with research scientists, ML engineers, and platform teams to transform experimental workloads into robust, repeatable pipelines and push the boundaries of the infrastructure's capabilities.
Responsibilities
- Design and implement infrastructure for large-scale experiments, data processing, and model training.
- Build abstractions for job submission, scheduling, and monitoring to enable rapid researcher iteration.
- Create tooling to enhance research developer productivity, including experiment management and CI/testing.
- Influence the long-term roadmap for research computation and AI model development.
- Provide technical mentorship to other engineers working on compute, infrastructure, and AI systems.
Requirements
- BS/MS or PhD in Computer Science or related field.
- 5+ years of software engineering experience, with significant experience in 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.
- Proficiency in systems programming languages (e.g., C++, Rust, Go, Java, Scala) and ability to design, implement, and debug complex services.
- Experience building or contributing to cluster schedulers, resource managers, or large-scale job orchestration systems (e.g., Kubernetes, Slurm, Ray).
- Understanding of modern ML training and inference workflows.
- Ability to move fast, be pragmatic, and maintain operational excellence.
- Experience driving complex systems from prototype to stable services.
- Clear communication skills with both researchers and engineers.