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HUD

AI agent evaluation platform and benchmarks

Talent Engineer

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

About the Job

Requirements

  • Experience hiring engineering, product, and go-to-market roles.
  • Ability to evaluate candidates through portfolios and projects, not only resumes.
  • Ability to understand and communicate technical concepts credibly with candidates.
  • Ability to manage multiple roles and recruiting pipelines simultaneously.
  • Experience in early-stage startups and ability to work independently in fast-paced environments.

Responsibilities

  • Own end-to-end recruiting across technical and go-to-market roles.
  • Build and optimize recruiting operations and tooling, automating workflows where possible.
  • Partner with founders and team leads to define role requirements, evaluation criteria, and interview processes.
  • Develop a strong understanding of the product to pitch technical candidates effectively.
  • Source top candidates through high-signal and unconventional channels.
  • Screen candidates for technical competence and product/market intuition.
  • Support onboarding and new-hire ramp.
  • Help define and run performance-review processes, leveling, and feedback systems.

Desired Qualifications

  • A track record of creative sourcing.
  • Familiarity with Ashby, Juicebox, and Clay.
  • Experience with vibe coding and using artificial intelligence to automate workflows.
  • Exposure to or ownership of people or human resources functions.
  • Experience evaluating non-traditional candidate profiles.

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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