Member of Technical Staff - ML & Data Infrastructure
The Member of Technical Staff - ML & Data Infrastructure role at xAI involves building foundational infrastructure to support advanced AI models and truth-seeking agents. The team focuses on large-scale data acquisition, web-scale search systems, high-throughput inference serving, and optimizing GPU/kernel performance. This position offers the opportunity to collaborate across various teams in a fast-paced environment, tackling complex problems with first-principles thinking.
Key responsibilities include designing and operating distributed systems for data acquisition and processing, developing high-performance search engines, creating reliable inference serving infrastructure, optimizing low-level GPU/kernel performance, innovating on compilers and runtimes, and managing complex workloads across cloud and cluster environments.
Required qualifications encompass strong systems engineering skills with a proven impact on large-scale distributed infrastructure, proficiency in Python and at least one compiled language (such as Rust, C++, Go, or Java), hands-on experience in areas like petabyte-scale data pipelines, web-scale search/retrieval, inference optimization, compiler features, or high-speed interconnects, a deep understanding of distributed systems challenges, and a passion for AI infrastructure.
The annual salary range for this position is $180,000 to $440,000 USD. The compensation package includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short and long-term disability insurance, life insurance, and various other discounts and perks.
xAI fosters a meritocratic environment where employees are expected to be hands-on and contribute directly to the company's mission. Leadership is granted to those who show initiative and consistently deliver excellence. The company values strong communication skills, work ethic, and prioritization abilities, offering growth opportunities for individuals who thrive on curiosity and challenging themselves.