Staff Software Engineer, AI Runtime

Databricks Inc.; Mountain View (CA); On-site; USD 190,000 - 265,000; Full time

Job summary

Databricks Inc. seeks a Staff Software Engineer to enhance and scale its AI Runtime platform for GPU training. The role involves driving architecture, solving complex training issues, and mentoring engineers.

Applicants should have 10+ years in distributed systems and experience with frameworks like PyTorch. A commitment to collaboration and communication across teams is essential. Local pay ranges from $190,000 to $265,000 USD.

Qualifications

• 10+ years of experience in large-scale distributed systems.
• Hands-on experience with distributed training frameworks.
• Strong understanding of training resilience patterns.

Responsibilities

• Drive architecture of AIR’s managed GPU training platform.
• Solve large-scale training problems and push GPU efficiency.
• Lead engineering efforts from design to production rollout.

Skills

Distributed training frameworks

GPU infrastructure

High-performance computing

Algorithms and data structures

Communication skills

Education

BS in Computer Science

MS or PhD preferred

Tools

PyTorch

DeepSpeed

FSDP

Megatron

Job description

P-1930

At Databricks, we are passionate about enabling data teams to solve the world’s 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 world’s 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 Staff 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 define the long‑term technical vision for AIR, mentor senior engineers, partner across product, research, and platform teams, and lead 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 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 help shape Databricks’ long‑term technical direction in AI training infrastructure.

What we look for:

• 10+ years of experience building and operating large‑scale distributed systems, with significant depth in GPU training infrastructure, high‑performance computing, or ML systems.
• Hands‑on 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.
• Strategic, product‑oriented mindset with the ability to align technical execution to a long‑term vision, and a passion for mentoring engineers and fostering technical excellence.
• BS in Computer Science or a related field (MS or PhD preferred).

Local Pay Range

$190,000 — $265,000 USD

About Databricks

Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow.

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, please refer to the internal resources.

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.