Senior Staff ML Engineer, Search & Recommendation

Reddit Reddit · Consumer · United States · Remote · Machine Learning

Senior Staff ML Engineer at Reddit focused on building and scaling the next generation of search and recommendation systems, integrating LLMs, and enhancing core retrieval and ranking. The role involves end-to-end ownership of search relevance, from query understanding to LLM-based answers and RAG.

What you'd actually do

  1. Contribute to advancing Reddit's Search and Recommendations products by designing AI-driven Search experience prioritizing seamless and delightful user experience.
  2. Deeply understand the Reddit search product and drive the vision for the search relevance team.
  3. Enhance core search retrieval and ranking, design and implement new search engine features, build and scale search indexes, develop and test new pipeline components. You will also deploy ML models, integrate LLMs, and ensure robust monitoring and smooth product integration throughout the process.
  4. Collaborate across disciplines and with ML, Product, Infrastructure, and DS teams at Reddit to find technical solutions to complex challenges.
  5. Mentor and guide senior and staff engineers in the team.

Skills

Required

  • large-scale search and recommendation systems
  • PyTorch or Tensorflow
  • LLM in production
  • lexical and semantic retrieval systems
  • Python
  • Golang

Nice to have

  • Agentic AI frameworks

What the JD emphasized

  • 10+ years of industry experience with deep expertise in large-scale search and recommendation systems
  • Proven ability to identify key opportunities, define roadmaps and drive scalable improvement in search relevance
  • Strong experience in building and deploying large-scale ML models using frameworks such as PyTorch or Tensorflow
  • Experience working with LLM in production, including evaluation, tuning and deployment
  • In-depth knowledge and experience working with lexical and semantic retrieval systems

Other signals

  • building large-scale systems
  • end-to-end search relevance
  • integrating LLMs
  • production LLM experience