Staff Engineer, Model Serving
Databricks12 months ago
Base Salary
$192k - $260k/yr
Responsibilities
- Design and implement core systems and APIs powering Databricks Model Serving.
- Define the technical roadmap and long-term architecture with product and engineering leadership.
- Make architectural trade-offs to optimize performance, throughput, autoscaling, and operational efficiency.
- Contribute to model container builds, deployment workflows, routing, caching, observability, and intelligent autoscaling.
- Collaborate with product, platform, infrastructure, and research teams to translate customer needs into reliable systems.
- Lead initiatives improving latency, availability, and cost-effectiveness across serving layers.
- Establish code quality, testing, and operational-readiness practices and mentor engineers.
- Represent the team in cross-organizational technical discussions and influence AI platform strategy.
Requirements
- 10+ years of experience building and operating large-scale distributed systems.
- Deep expertise in model serving, inference systems, and infrastructure such as routing, scheduling, autoscaling, and observability.
- Strong knowledge of algorithms, data structures, and system design for large-scale, low-latency serving systems.
- Experience leading architecture for large-scale, performance-sensitive CPU/GPU inference systems.
- Proven ability to deliver technically complex, high-impact initiatives with measurable customer or business value.
- 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.
Benefits
- Comprehensive benefits and perks, with region-specific details available through Databricks' benefits portal.
Tech Stack
Apache SparkMLflow
Categories
About Databricks
Databricks builds a cloud-based data and AI platform centered on the lakehouse architecture, combining data engineering, analytics, and machine learning with Apache Spark, Delta Lake, and MLflow. It sells subscriptions and cloud services to enterprises that need to unify data pipelines and develop large-scale AI and analytics. Founded in 2013 by the creators of Apache Spark and headquartered in San Francisco, the company is privately held and serves organizations across many industries.