Job description

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). 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 $190,000 — $265,000 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 . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . 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.

Requirements

Must-haves

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.
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.
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.
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.
Our Commitment to Diversity and Inclusion

Nice-to-haves

BS in Computer Science or a related field (MS or PhD preferred).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aiml

Platform: greenhouse
Effort: quick