Research Scientist, Frontier Risk Evaluations

Scale AI Scale AI · Data AI · San Francisco, CA · Research

Research Scientist role focused on designing and building evaluation measures, harnesses, and datasets for frontier AI systems, with a focus on identifying and mitigating risks. The role involves collaboration with external agencies and publishing findings, bridging AI research and policy.

What you'd actually do

  1. Design and build harnesses to test AI models and systems (including agents) for dangerous capabilities such as security vulnerability exploitation, CBRN uplift, and other high-risk activities
  2. Work with government agencies or other labs to collectively scope and design evaluations to measure and mitigate risks posed by advanced AI systems
  3. Publish evaluation methodologies and write technical reports for policymakers

Skills

Required

  • design and build harnesses to test AI models and systems
  • instrumenting ML pipelines
  • writing evaluation harnesses
  • published research in machine learning, particularly in generative AI
  • addressing sophisticated ML problems
  • written and verbal communication skills

Nice to have

  • crafting evaluations and benchmarks
  • data science roles related to LLM technologies
  • red-teaming or adversarial testing of AI systems
  • AI safety policy frameworks

What the JD emphasized

  • design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems
  • design and build harnesses to test AI models and systems (including agents) for dangerous capabilities
  • Publish evaluation methodologies and write technical reports for policymakers
  • practical experience conducting technical research collaboratively
  • building and instrumenting ML pipelines
  • writing evaluation harnesses
  • quickly turning new ideas from the research literature into working prototypes
  • track record of published research in machine learning, particularly in generative AI
  • addressing sophisticated ML problems
  • crafting evaluations and benchmarks
  • red-teaming or adversarial testing of AI systems
  • AI safety policy frameworks

Other signals

  • design and create evaluation measures, harnesses and datasets for measuring the risks posed by frontier AI systems
  • design and build harnesses to test AI models and systems (including agents) for dangerous capabilities
  • publish evaluation methodologies and write technical reports for policymakers