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HUD

AI agent evaluation platform and benchmarks

Growth Lead

Generalist RecruitingConfirmed live in the last 24 hours
Full-timeSan Francisco, CA, USA

About the Job

Requirements

  • Experience finding and connecting with people in any context, such as recruiting or go-to-market.
  • Experience in an early-stage startup and ability to work independently in fast-paced environments.

Responsibilities

  • Tell HUD’s story and get people excited about its vision through worldbuilding.
  • Find, connect with, and meet candidates, AI engineers and researchers, and other key people in the AI ecosystem.
  • Define the company’s culture and values, screen candidates, and develop an onboarding process for new hires based on them.
  • Work with the Chief of Staff to own end-to-end recruiting, including building and optimizing recruiting operations, tooling, and automated workflows.
  • Work with the Marketing & Events Lead to host community events, represent HUD at industry events, and create content.
  • Work with the go-to-market engineers to find introductions or connection pathways into high-priority leads.

Desired Qualifications

  • A strong network within reinforcement learning researcher, reinforcement learning engineer, and broader startup communities.
  • A clear point of view on how great technical companies tell stories.
  • Experience in recruiting, community, or content roles in startups.
  • Evidence of being able to understand technical products well enough to explain them simply and accurately.
  • A standout portfolio of writing that showcases writing ability.

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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