Senior Software Engineer, AI Runtime

P-1428

At Databricks we are passionate about enabling data teams to solve the worlds toughest problems from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the worlds best data and AI infrastructure platform so our customers can use deep data insights to improve their business.

Training and customizing state-of-the-art AI models is one of the most demanding workloads in computing and it sits at the heart of Databricks Mosaic AI mission. AI Runtime (AIR) is our managed platform for large-scale GPU training and fine-tuning. It gives customers on-demand access to fleets of the latest accelerators and a serverless experience that hides the complexity of provisioning scheduling and orchestrating multi-node jobs with the resilience to keep training running for days or weeks across thousands of GPUs. AIR powers the full spectrum of custom training from fine-tuning open models to pre-training frontier-scale foundation models for some of the most sophisticated AI teams in the world.

As a Senior Software Engineer for AI Runtime you will play a critical role in building and scaling the systems that make large-scale training fast reliable and effortless. You will drive the architecture and evolution of the managed GPU training stack spanning scheduling and capacity distributed training performance fault tolerance and the developer experience of launching and operating jobs at scale. Beyond hands-on contributions to core systems you will help shape the technical direction for AIR mentor other engineers partner across product research and platform teams and contribute to the initiatives that expand the technical and business impact of custom training at Databricks.

The impact you will have:

• Drive the architecture and evolution of AIRs 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 contribute to Databricks technical direction in AI training infrastructure.

What we look for:

• 5 years of experience building and operating large-scale distributed systems with experience in GPU training infrastructure high-performance computing or ML systems.
• 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.
• Customer-focused mindset with the ability to align implementation details with product goals and a passion for mentoring engineers and fostering technical excellence.
• BS in Computer Science or a related field (MS or PhD preferred).