Software Engineer - Customer Engagement & Docs Platform

Who we are

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.

Our AI-powered Assistant serves as the initial point of contact for customers seeking assistance on how to use our platform effectively. The Customer Engagement and Docs platform team's objective is to help customers resolve their problems faster using AI / LLMs. We provide customers with precise and comprehensive solutions to their problems or build experiences to facilitate a seamless escalation to a support agent when necessary.

As a Staff Engineer, you will operate at the intersection of Product development and Machine Learning. Responsibilities include gaining a deep understanding of user behavior; identifying factors contributing to problems; leveraging Product, Machine Learning (ML) and Large Language Models (LLMs) to analyze intricate technical use cases within a secure enterprise environment and eventually providing a path to resolving them.

• Lead design, development, and deployment of systems for Evaluation, Answer retrieval and Quality improvement for helping customers.
• Lead architectural decisions to ensure performance, reliability, and accuracy of the knowledge systems.
• Drive best practices for engineering excellence, including design reviews, code quality, testing strategies, and performance optimizations.
• Deliver high-quality, production-ready code and services end-to-end — including performance tuning, resiliency improvements, and debugging in live environments.
• Contribute to longer-term technical planning and support strategic initiatives in Assistant and Support organizations.

• 6+ years of industry experience building and operating large-scale distributed systems.
• Demonstrated technical proficiency in machine learning and software engineering, coupled with a strong product-oriented approach.
• The capacity to define and structure solutions in ambiguous problem domains.
• Ability to mentor and guide junior engineers, as well as to effectively collaborate across various team boundaries.
• Track record of driving high-impact, technically complex initiatives that delivered clear customer or business value.
• Prior experience with retrieval systems and search technologies is a plus.

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.

$190,000 — $260,000 USD

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.

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