Staff Software Engineer, AI Runtime
Databricks · Mountain View, California; San Francisco, California · Engineering · listed June 8, 2026
The shape of it
Seniority
Staff
Where
Not stated
Stated pay
$190,000 – $265,000 USD
Requirements listed
8
Length
824 words
In the posting’s own words
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.
What it asks for · 8
- 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).
What the job covers
- 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.
Degree language
- BS in Computer Science or a related field (MS or PhD preferred).
Tools and skills named
Models & research
- GPU7×
- Fine-tuning
- Machine learning
Cloud & infra
- Distributed systems
- Observability
- Serverless
Data
- Databricks3×
Frameworks
- Node.js
Words the posting leans on
- training10×
- models7×
- gpu6×
- runtime5×
- technical5×
- systems4×
- architecture3×
- customers3×
- engineers3×
- next3×
- product3×
- accelerator2×
- agents2×
- already2×
- build2×
- checkpointing2×
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