Staff Partner Engineer, AI Partnerships

RDQ427R534

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. Founded by engineers — and customer obsessed — we leap at every opportunity to tackle 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.

We're looking for a Partner Engineer to lead the technical relationship between Databricks and our strategic AI partnerships - including Anthropic, OpenAI and Google Gemini. You'll own the joint roadmap with their product teams, drive the development of integrations that connect and extend both platforms, and get hands-on with new product features and tooling. You'll collaborate with partners to build POCs that become new products, demos, and hands-on labs - and ultimately the architectures our customers deploy. With direct visibility and engagement with our founders and executives, this high impact role sits in Partner Engineering and works closely with Product, Engineering, Strategic Partnerships and Global GTM teams.

• Serve as the primary technical point of contact across the AI labs, building relationships with senior product and engineering stakeholders to drive joint innovation.
• Work alongside our technical executives to shape the joint product roadmap by translating partner capabilities and integration needs into Databricks platform requirements
• Navigate complex relationships in order to align on joint product offerings that address the needs of Databricks, our partner organizations, and our customers.
• Design and build the integrations that connect and extend both platforms, serving as the hands-on technical expert and early validator of new capabilities.
• Build POCs and reference architectures that hand off cleanly to engineering teams to productize, or to customers to deploy.
• Work with our most strategic customers to align their AI architecture and roadmap needs with what Databricks and our partners are building.
• Develop and maintain technical field enablement content to equip internal & external field teams with the knowledge and tools to deliver joint architectures and articulate differentiated 'better together' value propositions.

• 10+ years in field engineering, consulting, platform architecture, or AI systems development, with the ability to master new technologies quickly while managing multiple partners.
• Depth in how AI systems are built and run in production, across model serving, inference, retrieval, and agent frameworks, and an understanding of how AI products are developed, delivered, and adopted.
• Hands-on engineering skills, with the ability to develop POCs, build demos, and debug integrations that hold up in front of partner engineering teams and customers. Experience with Python and REST APIs and SDKs.
• Established existing relationships within the product and engineering functions of major AI labs and cloud providers.
• Experience in strategic technical partnerships, navigating multi-company engineering initiatives, building consensus across organizations, and leading architectural discussions with clarity.
• Deep knowledge of the Databricks platform and the ability to design AI architectures that span it and a partner's products.
• Communication and influence skills, with the ability to present to engineering teams and executives, and to translate between Product, Engineering, and partner teams.
• Availability to travel globally to partner sites, conferences, and events as required.

• Prior experience in Partner Solutions Architect, Solutions Engineering, or Alliance Engineering roles at AI companies, cloud providers, global system integrators, or major enterprise software companies, with a track record of driving product-to-product integrations.
• Experience taking AI systems to production at enterprise scale, including evaluation, cost and performance tuning, and the security and governance requirements that gate deployment.
• Background working at or with a frontier lab or AI-native cloud provider, including an understanding of how they build, ship, and go to market.
• Track record of creating technical content that developers use, including reference architectures, deployment guides, blog posts, and conference presentations.
• Strong familiarity with modern development workflows and AI-assisted development tools such as Cursor, Claude Code, and Codex.