Staff Data Scientist - Infrastructure

About Glean: Glean is the Work AI platform that helps everyone work smarter with AI. What began as the industry’s most advanced enterprise search has evolved into a full-scale Work AI ecosystem, powering intelligent Search, an AI Assistant, and scalable AI agents on one secure, open platform. With over 100 enterprise SaaS connectors, flexible LLM choice, and robust APIs, Glean gives organizations the infrastructure to govern, scale, and customize AI across their entire business - without vendor lock-in or costly implementation cycles. At its core, Glean is redefining how enterprises find, use, and act on knowledge. Its Enterprise Graph and Personal Knowledge Graph map the relationships between people, content, and activity, delivering deeply personalized, context-aware responses for every employee. This foundation powers Glean’s agentic capabilities - AI agents that automate real work across teams by accessing the industry’s broadest range of data: enterprise and world, structured and unstructured, historical and real-time. The result: measurable business impact through faster onboarding, hours of productivity gained each week, and smarter, safer decisions at every level. Recognized by Fast Company as one of the World’s Most Innovative Companies (Top 10, 2025), by CNBC’s Disruptor 50, Bloomberg’s AI Startups to Watch (2026), Forbes AI 50, and Gartner’s Tech Innovators in Agentic AI, Glean continues to accelerate its global impact. With customers across 50+ industries and 1,000+ employees in more than 25 countries, we’re helping the world’s largest organizations make every employee AI-fluent, and turning the superintelligent enterprise from concept into reality. If you’re excited to shape how the world works, you’ll help build systems used daily across Microsoft Teams, Zoom, ServiceNow, Zendesk, GitHub, and many more. You'll be deeply embedded where people get things done. You'll ship agentic capabilities on an open, extensible stack, with

P-186 At Databricks, we are obsessed with enabling data teams to solve the world's toughest problems, from security threat detection to cancer drug development. We do this by building and running the world's best data and AI infrastructure platform, so our customers can focus on the high value challenges that are central to their own missions. Founded in 2013 by the original creators of Apache Spark™, Databricks has grown from a tiny corner office in Berkeley, California to a global organization with over 1000 employees. Thousands of organizations, from small to Fortune 100, trust Databricks with their mission-critical workloads, making us one of the fastest growing SaaS companies in the world. Our engineering teams build highly technical products that fulfill real, important needs in the world. We constantly push the boundaries of data and AI technology, while simultaneously operating with the resilience, security and scale that is critical to making customers successful on our platform. We develop and operate one of the largest scale software platforms. The fleet consists of millions of virtual machines, generating terabytes of logs and processing exabytes of data per day. At our scale, we regularly observe cloud hardware, network, and operating system faults, and our software must gracefully shield our customers from any of the above. Modern data analysis employs sophisticated methods such as machine learning that go well beyond the roll-up and drill-down capabilities of traditional SQL query engines. As a software engineer on the Runtime team at Databricks, you will be building the next generation distributed data storage and processing systems that can outperform specialized SQL query engines in relational query performance, yet provide the expressiveness and programming abstractions to support diverse workloads ranging from ETL to data science. Below are some example projects: Apache Spark™ : Develop the de facto open source standard framework for big data. Da

Location: San Francisco or Mountain View, California GAQ327R225 Databricks is building an Agentic Enterprise applications Platform a scalable, governed AI application platform built on Databricks that enables any internal team (GTM, Finance, HR, Legal, Product) to build production grade agentic applications in weeks, not months. The platform provides a managed agent runtime, standardized MCP connectors to enterprise systems of record, a shared UI component library with a design system, an intelligence data/context layer and a gold-standard promotion pipeline from prototype to production. As a Staff Product Manager , you will own the product strategy, roadmap, and execution for the Agentic Platform the foundational layer that every domain workspace depends on. You will work across agent runtime, MCP connectors, intelligence layer, evaluation framework, developer experience, and governance to deliver a platform that reduces time-to-production from months to weeks while maintaining enterprise grade quality, security, and reliability. You will partner closely with Application Engineering, the CIO organization, and domain teams across GTM, HR, Finance, and Product to ensure the platform serves real needs and scales with the organization. The impact you will have: Own the Agentic Platform strategy and roadmap. Define what ships, in what order, and why. Translate organizational outcomes into concrete platform capabilities with measurable success criteria. Define and drive the agent and runtime. Establish the managed agent runtime supporting multi step orchestration with durable execution, model gateway abstraction across all providers, governed tool invocation, and configurable per-agent guardrails (cost ceilings, timeouts, blast radius limits). Build the MCP connector ecosystem. Own the strategy for standardized, bidirectional connectors various systems of record. Drive on behalf of identity propagation, idempotency, dry-run/preview mode, and a connector SDK

RDQ427R613 About Databricks Databricks is the Data and AI company. More than 10,000 organizations worldwide rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics, and AI. As agents move from demos to real production work, Databricks is building the platform that lets enterprises create, govern, and collaborate with coding agents safely and at scale. Team & Product Omnigent is Databricks' platform for building, composing, governing, and sharing AI coding agents. It lets people create and coordinate agents across different model providers and execution environments - from the CLI and IDE to desktop, mobile, and managed enterprise surfaces. Users can watch agents work live, jump in to co-drive or fork a session, and package and share reusable agents. This is a rare chance to define and refine interaction patterns at enterprise scale: how people supervise fleets of agents, intervene at the right moment, understand risk, and build trust in autonomous work - starting with coding, review, and exploration workflows. The hard design problem isn't just showing what agents are doing; it's helping people know when to trust, when to step in, and how to recover when work goes off track - across a high-density desktop workspace and lightweight mobile moments. You'll design for two audiences at once: an open-source community shaping the future of agent workflows, and enterprise teams adopting agents in governed production. Role You'll be the IC design lead for how humans and fleets of agents work together on Omnigent - leading through product strategy, systems thinking, craft, and cross-functional collaboration. You're equally comfortable shipping small design changes directly in the codebase and prototyping your way through big, ambiguous problems that don't have a spec yet. You'll own the design direction across desktop and mobile, partner deeply with engineering and product, and translate the frontier of what agents can do into experience

RDQ427R70 Our mission at Databricks is to radically simplify the data lifecycle with a unified Lakehouse platform for data engineering, analytics, and AI. The Lakehouse addresses major challenges in enterprise data platforms, including reliability, data staleness, operational complexity, total cost of ownership, and data lock-in. Learn more about the Lakehouse architecture. Lakeflow is a critical part of this vision, helping customers build and operate streaming and batch ETL pipelines that power business-critical data products. As these workloads become increasingly mission-critical, customers need them to continue operating through infrastructure failures and cloud-region outages. As a Staff Engineer on the part of the Lakeflow Disaster Recovery team, you will design and implement distributed systems that replicate and recover pipelines across regions. A pipeline is more than source code and output tables: it includes streaming checkpoints, source offsets, stateful operator state, table versions, transaction metadata, schedules, and dependencies across a dataflow graph. You will solve challenging problems involving consistency, idempotency, causal ordering, failover, failback, and safe recovery without silent data loss or duplication. You will work in one or more of the following areas: Cross-region replication and recovery for Lakeflow pipelines, streaming tables, and materialized views Distributed consistency across pipeline dependencies, table versions, and transaction logs Failover and failback workflows with conservative correctness guardrails Deep clone, metadata reconciliation, observability, and failure-injection testing High-fidelity recovery simulations, game-day testing, and formal reasoning about failure modes What we look for: A passion for distributed systems, databases, storage systems, streaming systems, or reliability engineering Strong software engineering skills in Java, Scala, C++, Go, Python, or a similar production language Understandin

P-188 Our mission at Databricks is to radically simplify the whole data lifecycle from ingestion to ETL, BI, and all the way up to ML/AI with a unified platform. To achieve this goal, we believe the data warehouse architecture as we know it today will be replaced by a new architectural pattern, Lakehouse ( CIDR 2021 paper ), open platforms that unify data warehousing and advanced analytics. The new architecture will help address several major challenges, including data staleness, reliability, total cost of ownership, data lock-in, and limited use-case support. A critical part of realizing this vision is the next generation (decoupled) query engine and structured storage system that can outperform specialized data warehouses in relational query performance, yet retain the expressiveness and of general purpose systems such as Apache Spark™ to support diverse workloads ranging from ETL to data science. As part of this team, you will be working in one or more of the following areas to design and implement these next gen systems that leapfrog state-of-the-art: Query compilation and optimization Distributed query execution and scheduling Vectorized execution engine Data security Resource management Transaction coordination Efficient storage structures (encodings, indexes) Automatic physical data optimization What we look for: A passion for database systems, storage systems, distributed systems, language design, or performance optimization Experience working towards a multi-year vision with incremental deliverables Motivated by delivering customer value and impact 8+ years of experience working in a related system (preferred) Optional: PhD in databases or distributed systems 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