Databricks

1 month ago

Staff Software Engineer, AI Runtime

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 5 hours agoStatus: Live

Job Summary

Build and scale Databricks AI Runtime (AIR) across large GPU training fleets, driving architecture, reliability, and developer experience for multi-node training. Lead end-to-end engineering, mentor engineers, and partner with product/research/platform teams to extend AIR, add accelerators, and expand regional coverage while delivering high performance and strong SLAs/SLOs.

Location: Mountain View, San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Manager level

Key Responsibilities

•Lead 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 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 APIs, CLI, and developer experience to launch, monitor, and debug production training jobs.

Key Requirements

•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 (NVLink, InfiniBand or RoCE), collective communication, and 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.

Experience:10+ yearsDistributed systemsGPU trainingML systemsCloud

Education:Bachelor's

Skills:CommunicationMentoringCollaborationLeadershipProblem-solving

Languages:English

Tech Stack:PyTorchFSDPDeepSpeedMegatronNVLinkInfiniBandRoCEGPUCUDA

Company Brief

Databricks

Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.

Industry: Data Infrastructure

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series E+

Headquarters: San Francisco, United States

Founded: 2013

Website LinkedIn

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Databricks

1 month ago

Staff Software Engineer, AI Runtime

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 5 hours agoStatus: Live

Job Summary

Location: Mountain View, San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Seniority: Manager level

Key Responsibilities

•Lead 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 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 APIs, CLI, and developer experience to launch, monitor, and debug production training jobs.

Key Requirements

•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 (NVLink, InfiniBand or RoCE), collective communication, and 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.

Experience:10+ yearsDistributed systemsGPU trainingML systemsCloud

Education:Bachelor's

Skills:CommunicationMentoringCollaborationLeadershipProblem-solving

Languages:English

Tech Stack:PyTorchFSDPDeepSpeedMegatronNVLinkInfiniBandRoCEGPUCUDA

Company Brief

Databricks

Provides a unified data analytics platform powered by Apache Spark to simplify building, deploying, and scaling data engineering, data science, and machine learning workloads for enterprises.

Industry: Data Infrastructure

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Scaleup

Valuation: Unicorn (USD 1B+)

Funding: Series E+

Headquarters: San Francisco, United States

Founded: 2013

Website LinkedIn