Staff Machine Learning Engineer (Search Ranking)

TL;DR

Staff Machine Learning Engineer (Search Ranking): Leading the development of next-generation search ranking systems to improve relevance, quality, and personalization at scale with an accent on learning-to-rank, deep retrieval, and neural ranking. Focus on building multi-objective ranking systems, optimizing large-scale feature pipelines, and integrating generative AI/LLMs into retrieval systems.

Location: Must be based in the US (Bellevue, Palo Alto, San Francisco, Santa Monica, or Seattle) with a requirement to work in the office 4+ days per week.

Company

A technology company powering the hirify.globalchat visual messaging app, Lens Studio AR platform, and Spectacles AR glasses.

What you will do

• Lead the design and development of ML models for search ranking, focusing on relevance, personalization, and intent understanding.
• Own major ranking initiatives from problem definition through experimentation, launch, and iteration.
• Build ranking systems that balance multiple objectives including relevance, user satisfaction, freshness, diversity, and safety.
• Partner with product managers and data scientists to define success metrics and the long-term ranking roadmap.
• Provide technical leadership across teams and mentor engineers working on ML ranking systems.
• Improve feature pipelines, training infrastructure, and model iteration velocity.

Requirements

• Bachelor's degree plus 8+ years of ML experience (or Master's + 7 years, or PhD + 4 years).
• Experience developing ML models for relevance ranking, personalization, or engagement optimization.
• Strong programming skills in Python, C++, Java, or Scala.
• Experience with large-scale ML infrastructure such as Spark, Flink, TensorFlow, PyTorch, or JAX.
• Proven ability to lead complex technical projects across multiple teams.
• Strong understanding of online experimentation, A/B testing, and metric design.

Nice to have

• Advanced degree in Computer Science, Machine Learning, Statistics, or Mathematics.
• Direct experience building Search ranking systems (query understanding, retrieval, re-ranking).
• Experience with ads, feed, or marketplace ranking systems.
• Knowledge of LambdaMART, transformer-based rankers, ANN search, or vector search.
• Experience with LLMs, semantic search, or retrieval-augmented generation (RAG).

Culture & Benefits

• Comprehensive medical coverage and emotional/mental health support programs.
• Paid parental leave and compensation packages including long-term success sharing.
• "Default together" culture emphasizing dynamic in-person collaboration.
• Commitment to diversity, equity, and inclusion in the workplace.