Staff Software Engineer, Observability

Databricks

P-150 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. As a software engineer with a backend focus, you will work closely with your team and product management to prioritize, design, implement, test, and operate micro-services for the Databricks platform and product. This implies, among others, writing software in Scala/Java, building data pipelines (Apache Spark™, Apache Kafka), integrating with third-party applications, and interacting with cloud APIs (AWS, Azure, CloudFormation, Terraform). Below are some example teams you can join: Data Science and Machine Learning Infrastructure: Build services and infrastructure at the intersection of machine learning and distributed systems. Our technology empowers the flagship collaborative workspace, notebooks, IDE integrations, and project management products. We also enable machine learning at scale with tools for environment management, distributed training, and managing the Machine Learning lifecycle through MLflow. Compute Fabric: Build the resource management infrastructure powering all the big data and machine learning workloads on the Databricks platform in a robust, flexible, secure, and cloud-agnostic way. The software manages millions of virtual machines. Data Plane Storage: Deliver reliable and high performance services and client libraries for storing and accessing humongous amount of data on cloud storage backends, e.g., AWS S3, Azure Blob Store. Enterprise Platform: Offer a simple and powerful experience for onboarding and managing all of their data teams across 10ks of users on the Databricks platform. We do this by building reliable, scalable services and infrastructure with intuitive UIs and by delivering high-impact, cross-cutting projects that drive the "land and expand" strategy for enterprise customers. Observability: Provide a world class platform for Databricks engineers to comprehensively observe and introspect their applications and services. We build scalable data-intensive infrastructure that processes huge amounts of logs and telemetry. By doing so, we enable teams to become more data-driven and build robust services. Service Platform: Build high-quality services and manage the services in all environments in a unified way. We provide engineers libraries, tools, services and guidance to develop reliable, scalable, and secure services. We build a unified platform for engineers to deploy and update their services across different clouds and environments. Core Infra: Build the core infrastructure that powers Databricks, making it available across all geographic regions and Cloud providers. We build highly available distributed systems, heavily utilizing cloud native projects, contributing back whenever possible. We run thousands of Kubernetes clusters across all regions and orchestrate millions of VMs on a daily basis. Competencies * BS/MS/PhD in Computer Science, or a related field * 10+ years of production level experience in one of: Java, Scala, C++, or similar language. * Comfortable working towards a multi-year vision with incremental deliverables. * Experience in architecting, developing, deploying, and operating large scale distributed systems. * Experience working on a SaaS platform or with Service-Oriented Architectures. * Good knowledge of SQL. * Experience with software security and systems that handle sensitive data. * Experience with cloud technologies, e.g. AWS, Azure, GCP, Docker, Kubernetes.   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 $192,000—$260,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. 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.

Databricks

P-1930 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. As part of the AI team, you'll build the platforms and products that power everything from data apps, AI agents, model training, model serving, and Vector Search. You'll be joining a high-agency, high-visibility team operating at the frontier of AI infrastructure — with deep ties to research, product, and real-world enterprise use cases. Databricks Mosaic AI is one of our fastest-growing businesses, helping thousands of our customers democratize AI within their organizations. We're building the products and infrastructure that power the next generation of AI. We're hiring across multiple teams in our AI Engineering org, including the FMAPI (Foundation Model APIs) team — the unified serving layer for large language models across real-time and batch inference, powering model inference at enterprise scale. We are looking to hire high-agency engineers who bridge the gap between technical execution and product strategy. THE IMPACT YOU WILL HAVE: * Build LLM infrastructure powering large-scale inference workloads for customers through partner models (OpenAI, Anthropic, Gemini) and self-hosted models (Qwen, GPT-OSS, Llama) * Shape the direction of the FMAPI product — from roadmap to execution — by leveraging deep customer empathy and direct engagement with enterprise users and model providers * 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 WHAT WE LOOK FOR: * 8+ years of experience in backend or infrastructure engineering * Experience with distributed systems, scalable APIs, or cloud-native infrastructure * Strong product and ownership mindset, with a focus on shipping user-facing value * Experience with real-time serving, ML infrastructure, or GPU orchestration * Familiarity with service-oriented architecture, deployment pipelines, and system observability * Strong programming skills in Scala, Go, or Python BONUS POINTS FOR: * Exposure to platforms like SageMaker, Vertex AI, or Azure ML * Built products that support AI workflows   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 $190,000—$265,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. 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.

Databricks

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’ Model Serving product provides enterprises with a unified, scalable, and governed platform to deploy and manage AI/ML models — from traditional ML to fine-tuned and proprietary large language models. It offers real-time, low-latency inference, governance, monitoring, and lineage. As AI adoption accelerates, Model Serving is a core pillar of the Databricks platform, enabling customers to operationalize models at scale with strong SLAs and cost efficiency. As a Staff Engineer, you’ll play a critical role in shaping both the product experience and the foundational infrastructure of Model Serving. You will design and build systems that enable high-throughput, low-latency inference across CPU and GPU workloads, influence architectural direction, and collaborate closely across platform, product, infrastructure, and research teams to deliver a world-class serving platform. The impact you will have: * Design and implement core systems and APIs that power Databricks Model Serving, ensuring scalability, reliability, and operational excellence. * Partner with product and engineering leadership to define the technical roadmap and long-term architecture for serving workloads. * Drive architectural decisions and trade-offs to optimize performance, throughput, autoscaling, and operational efficiency for CPU and GPU serving workloads. * Contribute directly to key components across the serving infrastructure — from model container builds and deployment workflows to runtime systems like routing, caching, observability, and intelligent autoscaling — ensuring smooth and efficient operations at scale. * Collaborate cross-functionally with product, platform, and research teams to translate customer needs into reliable and performant systems. * Lead technical initiatives that improve latency, availability, and cost-effectiveness across both customer-facing and foundational serving layers. * Establish best practices for code quality, testing, and operational readiness, and mentor other engineers through design reviews and technical guidance. * Represent the team in cross-organizational technical discussions and influence Databricks’ broader AI platform strategy. What we look for: * 10+ years of experience building and operating large-scale distributed systems. * Deep expertise in model serving, inference systems, and related infrastructure (e.g., routing, scheduling, autoscaling, and observability). * Strong foundation in algorithms, data structures, and system design as applied to large-scale, low-latency serving systems. * Proven ability to deliver technically complex, high-impact initiatives that create measurable customer or business value. * Experience leading architecture for large-scale, performance-sensitive CPU/GPU inference systems. * Strong communication skills and ability to collaborate across teams in fast-moving environments. * Strategic and product-oriented mindset with the ability to align technical execution with long-term vision. * Passion for mentoring, growing engineers, and fostering technical excellence.   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 $192,000—$260,000 USD About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. 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.