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 and AI 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. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started.
Задачи
Build infrastructure that powers our flagship offerings like MLflow, AI Gateway, Databricks Apps, Agent Framework, Agent Bricks, and Foundation Model APIs, to state a few
Improve reliability, latency, and efficiency of distributed AI workloads
Collaborate with platform, infra, and ML teams to deliver seamless end-to-end experiences
Shape how developers and data scientists build and interact with AI on Databricks
Требования
5+ years of experience in backend or infrastructure engineering
Strong programming skills in Scala, Go, or Python
Experience with distributed systems, scalable APIs, or cloud-native infrastructure
Familiarity with service-oriented architecture, deployment pipelines, and system observability
Strong product and ownership mindset — you care about building the right solution, not just any solution
Будет плюсом
Experience with real-time serving, ML infrastructure, or GPU orchestration
Exposure to platforms like SageMaker, Vertex AI, or Azure ML
Contributions to OSS projects like MLflow, PyTorch, or Ray
Built developer platforms or internal tools supporting AI workflows
Условия
At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees
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greenhouseОсновная публикация · 2026-01-16
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