Staff Software Engineer, Search Evaluation and Quality Team

Google Google · Big Tech · New York, NY +1

Staff Software Engineer on the Search Evaluation Infrastructure team, focusing on pioneering and institutionalizing Evaluation-Driven Development (EDD) for generative AI features in Google Search. The role involves maximizing fine-tuned evaluation model quality, streamlining upstream data signals, and establishing precise AutoRater alignment. Responsibilities include leading AutoRater hillclimb, managing data contributions, aligning evaluation metrics with user growth, developing automated rating infrastructure, and shifting evaluations left to eliminate subjective assessments.

What you'd actually do

  1. Maximize fine-tuned evaluation model quality, streamline data upstream signals, and establish precise AutoRater alignment in collaboration with horizontal teams.
  2. Lead the core vertical AutoRater hillclimb and manage continuous vertical data contributions while scaling evaluation capabilities across different requirements.
  3. Align evaluation metrics with long-term user growth metrics and direct user feedback signals to bridge the functionality readiness gap before feature launches.
  4. Develop scalable automated rating infrastructure, high-quality evaluation rubrics, and continuous tracking systems to enable objective, metrics-based exptertise.
  5. Shift evaluations left to eliminate subjective quality assessments and implement metrics-based expertise starting with critical dimensions like "helpfulness."

Skills

Required

  • Bachelor’s degree or equivalent practical experience.
  • 8 years of experience in software development.
  • Experience integrating generative AI tools or LLM interfaces into workflows.
  • Experience with machine learning model training or fine-tuning (e.g., LLMs, generative AI).
  • Experience developing or executing evaluation frameworks or metrics for machine learning models.

Nice to have

  • Experience with LLM auto-rater architectures or agentic evaluation methodologies.
  • Experience driving the technical design, implementation, and deployment of ambiguous infrastructure projects from end to end.
  • Experience transforming technical innovations into user-facing products.
  • Experience using artificial intelligence assistance tools to prototype and deliver software features.
  • Experience collaborating across engineering teams to prioritize and execute project milestones.

What the JD emphasized

  • Evaluation-Driven Development (EDD)
  • fine-tuned evaluation model quality
  • AutoRater alignment
  • evaluation metrics
  • automated rating infrastructure
  • objective, metrics-based expertise
  • eliminate subjective quality assessments

Other signals

  • Evaluation-Driven Development (EDD)
  • fine-tuned evaluation model quality
  • AutoRater alignment
  • scalable automated rating infrastructure