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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.
About Databricks
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.
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.
Skills Required
- 10+ years of experience building and operating large-scale distributed systems
- Deep expertise in model serving, inference systems, routing, scheduling, autoscaling, and observability
- Strong foundation in algorithms, data structures, and system design for large-scale, low-latency serving systems
- Experience delivering technically complex, high-impact initiatives with measurable customer or business value
- Experience leading architecture for large-scale, performance-sensitive CPU/GPU inference systems
- Strong communication and cross-functional collaboration skills
- Strategic and product-oriented mindset aligned with long-term technical vision
- Passion for mentoring engineers and fostering technical excellence
Databricks Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Databricks and has not been reviewed or approved by Databricks.
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Healthcare Strength — Healthcare coverage is described as comprehensive, including medical, dental, vision, and mental-health support with EAP and wellness stipends. Feedback suggests these offerings are a core strength across locations, though specifics may vary by region.
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Equity Value & Accessibility — Equity and annual bonuses are highlighted as competitive and a major draw, aligning employee success with company growth. Feedback suggests stock options are seen as lucrative, particularly for certain roles and incumbents.
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Leave & Time Off Breadth — Flexible/unlimited PTO and robust paid parental leave are called out as notable components of the package. Feedback suggests these policies support flexibility alongside performance expectations.
Databricks Insights
What We Do
As the leader in Unified Data Analytics, Databricks helps organizations make all their data ready for analytics, empower data science and data-driven decisions across the organization, and rapidly adopt machine learning to outpace the competition. By providing data teams with the ability to process massive amounts of data in the Cloud and power AI with that data, Databricks helps organizations innovate faster and tackle challenges like treating chronic disease through faster drug discovery, improving energy efficiency, and protecting financial markets.
