Staff Software Engineer - Customer Engagement & Docs Platform

Databricks Databricks · Data AI · San Francisco, CA · Engineering - Pipeline

Databricks is seeking a Staff Software Engineer to work on their AI-powered Assistant, focusing on customer engagement and documentation platform. The role involves leveraging ML and LLMs to analyze technical use cases, identify problems, and provide solutions or facilitate escalation. Key responsibilities include leading the design, development, and deployment of systems for evaluation, answer retrieval, and quality improvement, as well as driving engineering best practices and contributing to technical planning.

What you'd actually do

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

Skills

Required

  • 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.

Nice to have

  • Prior experience with retrieval systems and search technologies is a plus.

What the JD emphasized

  • secure enterprise environment
  • Evaluation
  • Answer retrieval
  • Quality improvement
  • performance, reliability, and accuracy
  • customer or business value

Other signals

  • AI-powered Assistant
  • LLMs
  • customer engagement
  • resolve problems faster