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DatabricksDatabricksMountain View, CA

Sr. Engineering Manager, AI Runtime

Lead engineering team building Databricks AI Runtime for large-scale GPU model training and fine-tuning. Own product experience, distributed training infrastructure, reliability, and roadmap while collaborating across product, research, and platform teams. Requires 8+ years software engineering and 3+ years management experience with deep GPU training expertise.

229k – 297k
On-site8+ YOEEngineering Management

About the role

Impact

  • Lead, mentor, and grow a high-performing engineering team responsible for the Custom Training product and its foundational infrastructure, including distributed training orchestration, cluster lifecycle, fault tolerance, and training efficiency.
  • Define and own the product and technical roadmap for AIR, balancing customer experience, functionality, and foundational investments.
  • Collaborate closely with product, research, platform, infrastructure teams, and customers to drive end-to-end delivery, from ideation and prioritization to launch and operation.
  • Drive architectural decisions and product design for managed GPU training at scale.
  • Advocate for customer needs through direct engagement, ensuring engineering decisions translate to clear product impact.
  • Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks.
  • Partner with recruiting to attract, hire, and develop top-tier engineering talent.

Requirements

  • 8+ years of software engineering experience, with 3+ years in engineering management.
  • Track record building and operating managed GPU training infrastructure at scale (100s/1000s GPUs).
  • Deep familiarity with distributed training frameworks (PyTorch, DeepSpeed, Composer, Megatron-LM) and parallelism strategies (FSDP, tensor/pipeline parallelism).
  • Experience with training resilience patterns: checkpointing, elastic training, and automated failure recovery for long-running jobs.
  • Understanding of GPU performance fundamentals including NCCL, interconnect topologies, and memory optimization.
  • Experience building platform products with clear SLAs where you've owned the customer experience, not just the backend.
  • Strong cross-functional leadership across platform, product, and research teams, with the ability to lead through ambiguity and deliver complex projects.
  • Excellent collaboration and communication skills across engineering, product, and research organizations.
  • BS/MS in Computer Science, Electrical Engineering, or related technical field.

Skills

PyTorchDeepspeedMegatron-LmFsdpNcclGpu TrainingDistributed TrainingCheckpointingElastic TrainingTensor ParallelismPipeline Parallelism

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