Sr. Engineering Manager, AI Runtime

Навыки

• API (интеграции)
• Коммуникация
• Data Lake
• Дообучение моделей
• Лидерство
• LLM
• Мониторинг и observability

О компании и продукте

• 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 .
• 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.

Задачи

• 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

Требования

• 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

Условия

• At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees

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