H

HUD

AI agent evaluation platform and benchmarks

Research Engineer - Benchmarks

AI ResearchConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Proficiency in Python, Docker, and Linux environments.
  • Published papers or technical blogs on relevant topics, such as public benchmarks and their limitations or model failure modes; applicants should link these in their application.
  • A strong understanding of what makes a benchmark realistic, reliable, and useful.
  • Experience working on environments and evaluations.
  • Curiosity and the ability to understand workflows across various domains.

Responsibilities

  • Own the design, implementation, and quality of HUD’s internal agent benchmarks.
  • Work with subject-matter experts to define tasks and create domain-specific benchmarks that evaluate agents on realistic workflows.
  • Build infrastructure to reliably run models and agents against benchmark tasks.
  • Develop metrics and analyses to understand benchmark difficulty, reliability, and failure modes.
  • Validate whether benchmark performance correlates with real-world evaluations, customer needs, and lab expectations.
  • Write clear documentation and benchmark reports that make results legible and credible to technical audiences.

Desired Qualifications

  • Be detail-oriented and able to spot subtle inconsistencies or edge cases in tasks.
  • Reason from first principles about task design, scoring, and failure modes.
  • Thrive in unstructured problem spaces.
  • Have early-stage startup experience and the ability to work independently in fast-paced environments.
  • Have strong communication skills for remote collaboration across time zones.

About the Company

HUD

AI agent evaluation platform and benchmarks

HUD provides an evaluation platform for AI agents that perform computer-use tasks. It offers an interface that connects to HUD evaluation environments, allowing users to run benchmarks across hundreds of environments and thousands of tasks. Users can integrate their agents using various adapters, and interact with the system through an asynchronous API for efficient experiments. The platform then collects telemetry and benchmarking results to inform agent improvements. HUD differentiates itself by concentrating on evaluating and improving knowledge-work agents across a wide range of tasks and environments, rather than just building models, and by enabling scalable, tool-enabled testing via adapters and an asynchronous workflow. The company’s goal is to help organizations and developers assess, compare, and enhance their AI agents so they perform better in real-world knowledge-work scenarios.

Company size
11-50
Company stage
Seed
Total funding
$130K
Headquarters
San Francisco, CA
Founded
2025

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