Databricks

2 weeks ago

Sr. Engineering Manager, AI Runtime

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Lead and grow the engineering team behind AI Runtime’s Custom Training product, owning both customer-facing experience and foundational infrastructure for managed GPU training. Define and drive the AIR product and technical roadmap, orchestrate distributed training, and improve cluster lifecycle, fault tolerance, and training efficiency. Partner across platform, product, research, and customers to deliver and operate resilient, scalable multi-node training systems with strong observability and reliability practices.

Location: Mountain View, San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Lead, mentor, and grow a high-performing engineering team for AIR Custom Training, 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 with product, research, platform, infrastructure teams, and customers to deliver end-to-end from ideation and prioritization to launch and operation.
•Drive architectural decisions and product design for managed GPU training at scale.
•Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks.

Pay and Benefits

Salary: USD 228,600 - 297,120

Equity and Bonus:Equity

Key Requirements

•8+ years of software engineering experience, including 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 such as checkpointing, elastic training, and automated failure recovery for long-running jobs.
•BS/MS in Computer Science, Electrical Engineering, or a related technical field.

Experience:8+ yearsAIMachine learningDeep learningLLMGPU trainingDistributed training

Education:Bachelor's in Computer Science or Electrical Engineering

Skills:LeadershipMentorshipCross-functional collaborationCommunicationDecision-making under ambiguity

Languages:English

Tech Stack:PyTorchDeepSpeedComposerMegatron-LMFSDPTensor parallelismPipeline parallelismNCCLLLMGPUsDistributed training orchestration

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

2 weeks ago

Sr. Engineering Manager, AI Runtime

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 11 hours agoStatus: Live

Job Summary

Location: Mountain View, San Francisco

Workplace: Onsite

Employment Type: Full time

Job Function: Data Science & Machine Learning

Seniority: Manager level

Key Responsibilities

•Lead, mentor, and grow a high-performing engineering team for AIR Custom Training, 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 with product, research, platform, infrastructure teams, and customers to deliver end-to-end from ideation and prioritization to launch and operation.
•Drive architectural decisions and product design for managed GPU training at scale.
•Build observability and reliability practices for long-running, multi-node training jobs, including checkpoint strategies, failure recovery, and operational runbooks.

Pay and Benefits

Salary: USD 228,600 - 297,120

Equity and Bonus:Equity

Key Requirements

•8+ years of software engineering experience, including 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 such as checkpointing, elastic training, and automated failure recovery for long-running jobs.
•BS/MS in Computer Science, Electrical Engineering, or a related technical field.

Experience:8+ yearsAIMachine learningDeep learningLLMGPU trainingDistributed training

Education:Bachelor's in Computer Science or Electrical Engineering

Skills:LeadershipMentorshipCross-functional collaborationCommunicationDecision-making under ambiguity

Languages:English

Tech Stack:PyTorchDeepSpeedComposerMegatron-LMFSDPTensor parallelismPipeline parallelismNCCLLLMGPUsDistributed training orchestration

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