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[Remote] Software Engineer - Performance PhD Candidates

Remote Full-time Live

Note: The job is a remote job and is open to candidates in USA. Databricks is a data and AI company that helps data teams solve complex problems by building a robust data and AI infrastructure platform. The performance engineer role involves evaluating product performance, identifying bottlenecks, and collaborating with teams to enhance scalability and efficiency across their software products.

Responsibilities

  • You will identify performance limitations of the entire stack based on telemetry, customer signals, POCs, and benchmarks to result in the best performing system across the industry, when resolved
  • You will set the performance expectations for all cross-cutting efforts early on through specialized benchmarks capturing the intended customer user journeys, and make sure they are met before deployed to customers
  • You will understand the performance characteristics of the compute instance types, storage layers, and all cloud services Databricks depends on and deploy solutions to meet the customer demand
  • You will work with customers to root cause and reduce performance problems during production, previews, and POCs

Skills

  • PhD in databases or systems
  • Knowledge of database systems, storage systems, distributed systems, and performance optimization
  • Motivated by delivering customer value and influence

Benefits

  • Annual performance bonus
  • Equity

Company Overview

  • Databricks is a data and AI platform that unifies data engineering, analytics, and machine learning on a lakehouse architecture. It was founded in 2013, and is headquartered in San Francisco, California, USA, with a workforce of 5001-10000 employees. Its website is https://www.databricks.com.
  • Company H1B Sponsorship

  • Databricks has a track record of offering H1B sponsorships, with 385 in 2025, 319 in 2024, 227 in 2023, 222 in 2022, 166 in 2021, 64 in 2020. Please note that this does not guarantee sponsorship for this specific role.
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