Staff Engineer, ML & Retrieval Systems

Databricks; California (MO); On-site; USD 190,000 - 260,000; Full time

Job summary

Databricks is seeking a Staff Engineer to operate at the intersection of product development and machine learning. You will study user behavior, identify factors driving problems, and apply ML and LLMs to analyze complex enterprise use cases within a secure environment to guide resolution.

You will lead design, deployment, and optimization of evaluation, answer retrieval, and knowledge-quality systems, setting architecture for performance and reliability while mentoring engineers and partnering

Qualifications

• 6+ years building and operating large-scale distributed systems.
• Strong ML and software engineering skills with a product focus.
• Able to define solutions in ambiguous domains.
• Excellent mentorship and cross-team collaboration.
• Track record of high-impact initiatives with customer value.
• Experience with retrieval systems and search tech is a plus.

Responsibilities

• Lead design, development, and deployment of Evaluation, Answer retrieval and Quality improvement systems.
• Set architectural direction for performance, reliability, and accuracy of knowledge systems.
• Drive engineering excellence with reviews, code quality, testing, and performance optimizations.
• Deliver production-grade code and services end-to-end including tuning and resiliency.
• Contribute to long-term technical planning and strategic initiatives in Assistant and Support teams.

Skills

Distributed systems

Machine learning

Software engineering

Product mindset

Mentoring engineers

Ambiguity handling

Tools

Search technologies

Job description

Databricks is seeking a Staff Engineer to operate at the intersection of product development and machine learning. You will study user behavior, identify factors driving problems, and apply ML and LLMs to analyze complex enterprise use cases within a secure environment to guide resolution.

You will lead design, deployment, and optimization of evaluation, answer retrieval, and knowledge-quality systems, setting architecture for performance and reliability while mentoring engineers and partnering