Staff Software Engineer, AI Runtime

Impact

• Drive the architecture and evolution of AIR's managed GPU training platform, delivering scalable, high-throughput, and resilient training across fleets that span thousands of accelerators.
• Solve the hardest problems in large-scale training, including multi-node orchestration, distributed parallelism strategies, GPU scheduling and dynamic routing, high-throughput data loading, and checkpoint and restore for very long-running jobs.
• Push GPU efficiency and training performance, raising utilization (such as model FLOPs utilization and end-to-end throughput) and lowering cost per training run across diverse model architectures and hardware generations.
• Build the resilience and observability foundations that keep multi-node jobs healthy, detecting and recovering from hardware and software failures with minimal disruption to customers.
• Partner with product, research, and platform teams to shape the APIs, CLI, and developer experience that make it easy to launch, monitor, and debug production training jobs.
• Lead end-to-end engineering efforts, from design through production rollout, holding a high bar for performance, correctness, and reliability.
• Make direct, high-impact contributions to the core systems behind AIR, and help bring up support for the latest accelerators and new regions as the fleet grows.
• Champion engineering excellence, mentor other engineers through design reviews and technical discussions, and help shape Databricks' long-term technical direction in AI training infrastructure.

Requirements

• 10+ years of experience building and operating large-scale distributed systems, with significant depth in GPU training infrastructure, high-performance computing, or ML systems.
• Hands-on experience with distributed training frameworks (such as PyTorch, FSDP, DeepSpeed, or Megatron) and the parallelism strategies (data, tensor, pipeline, and sequence parallelism) used to train large models.
• Strong understanding of training resilience patterns, including checkpointing, failure detection, and automatic recovery for long-running, multi-node jobs.
• Solid grasp of GPU performance fundamentals, including accelerator architecture, high-speed interconnects (such as NVLink and InfiniBand or RoCE), collective communication, and the bottlenecks that govern training throughput and utilization.
• Experience building and operating managed, multi-tenant platform products in the cloud, with clear SLAs and SLOs for availability, performance, and reliability.
• Strong foundation in algorithms, data structures, and system design as applied to performance-sensitive, large-scale distributed systems.
• Proven ability to deliver technically complex, high-impact initiatives that create clear customer or business value.
• Strong communication skills and the ability to collaborate across product, research, and infrastructure teams in a fast-moving environment.
• Strategic, product-oriented mindset with the ability to align technical execution to a long-term vision, and a passion for mentoring engineers and fostering technical excellence.
• BS in Computer Science or a related field (MS or PhD preferred).