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xAI

Software Engineer - Search Ranking

Palo Alto, California, United States$180k–$440kmidAdded yesterday

About this role

Build and optimize the search ranking systems powering X's discovery engine, combining large-scale information retrieval infrastructure with machine learning models and AI reasoning capabilities. You'll own the full search stack from indexing and retrieval through ranking and user experience.

What you'll do

  • Design and implement search and retrieval algorithms serving millions of concurrent users
  • Build and operate large-scale search infrastructure and indexing systems
  • Train and deploy state-of-the-art ML ranking models
  • Integrate Grok AI reasoning into search products and systems
  • Take end-to-end ownership of search system reliability and performance
  • Collaborate across teams to improve relevance and search quality

What they're looking for

  • Python, Go, or Rust
  • Vector databases and search indices
  • Large-scale distributed systems
  • Machine learning model training and deployment
  • Information retrieval and ranking algorithms
  • Search infrastructure and indexing
  • Problem-solving and rapid learning
  • Communication and collaboration

Benefits

  • Equity compensation
  • Comprehensive medical, vision, and dental coverage
  • 401(k) retirement plan
  • Short and long-term disability insurance
  • Life insurance
  • Various discounts and perks
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xAI

xAI builds advanced AI infrastructure and systems, including the Grok model inference platform and Colossus GPU cluster. The company is hiring Mechanical, Electrical, and Facilities Engineers to design and maintain its data center operations, as well as Software Engineers to optimize high-performance inference systems and datacenter networking.

View all jobs at xAI

Likely interview questions

  • Describe a large-scale search system you've built or worked on—what were the biggest challenges in retrieval latency and ranking quality?
  • How have you approached training and evaluating ML ranking models in production, and what metrics did you optimize for?