P-1930

At Databricks, we run the world's largest AI platform. We believe the next breakthroughs will come from models trained on domain-specific enterprise data, not from bigger general-purpose models alone.

That's why we built AI Runtime, a serverless product that makes GPU training of any size feel simple, so companies can train their own models the way frontier labs do. Customers across industries train on it, right next to the enterprise data already in Databricks. Among them are Rivian in automotive, Robinhood in fintech, and Merck, which builds foundation models for drug discovery. Our own AI researchers use it to post-train models with hundreds of billions to nearly a trillion parameters.

It takes a lot of engineering to make that work, and we've written about it on our blog: how we keep GPUs reliable, make training fault-tolerant, and speed up fine-tuning with fused kernels and smart sequence packing. AI agents now launch experiments too, and on our research team they've already increased training jobs 8x.

As a Staff Software Engineer, you'll work on the systems behind all of this, solving hard technical problems and shaping major parts of AI Runtime.

The impact you will have:

• Drive major parts of AI Runtime's architecture and help shape Databricks' long-term technical direction in AI training infrastructure.
• Design the reliability and observability systems for a fleet that keeps growing in scale and complexity.
• Lead improvements to AI Runtime's developer experience for people and agents, so even the largest training jobs feel as simple as a first experiment.
• Help choose the technical bets that prepare AI Runtime for new GPU generations and workload types.
• Take on the hardest multi-node training problems yourself, from orchestration and GPU scheduling to data loading and checkpointing.
• Make training faster and cheaper by raising efficiency across model architectures and GPU accelerators.
• Work with research, product, and customers to shape what we build next and lead projects across teams.
• Champion engineering excellence, raise the bar, and mentor the engineers around you.

What we look for:

• 10+ years in large-scale distributed systems, with significant depth in GPU training, high-performance computing, or ML systems.
• Hands-on experience training large models with FSDP, DeepSpeed, or Megatron, and fluency in parallelism strategies.
• Expertise in GPU performance, including accelerator architecture, high-speed interconnects, and kernels.
• Strong understanding of training resilience patterns, such as checkpointing, failure detection, and automatic recovery.
• A background in building and running multi-tenant cloud platforms as products.
• A track record of leading technical projects across teams and seeing them through to customer impact.
• The clarity to bring teams to agreement on technical designs, and a reputation as a mentor other engineers seek out.
• BS in Computer Science or a related field (MS or PhD preferred).