Job description

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. Databricks' AI Runtime (AIR) product provides enterprises with an API for training and fine-tuning deep learning and LLM models with on-demand GPUs. Whether it's a transformer model for drug discovery or a fine-tuned foundation model, customers use this team's training infrastructure to build state-of-the-art frontier models. As a Senior Engineering Manager, you will lead the team owning both the product experience and the foundational infrastructure of AIR. You'll shape customer-facing capabilities while designing for scalability, extensibility, and performance of GPU training and adjacent areas, collaborating closely across the platform, product, infrastructure, and research organizations. The impact you will have: 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. What we look for: 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. Pay Range Transparency Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles. Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . Local Pay Range $228,600 — $297,120 USD About Databricks Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT&T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . 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 click here . 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.

Requirements

Must-haves

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.
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.
Our Commitment to Diversity and Inclusion

Nice-to-haves

None flagged.

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

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Platform: greenhouse
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