Senior Software Engineer, AI Runtime

The impact you will have:

• 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 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).

Pay Range Transparency

Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here.

Local Pay Range

$160,000—$225,000 USD

About Databricks

Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn, X, YouTube, and Instagram.

Our Commitment to Diversity and Inclusion

At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics.

Compliance

If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.