H

HUD

AI agent evaluation platform and benchmarks

Lead Research Engineer - Data Quality

AI ResearchConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • Advanced proficiency in Python, Docker, and Linux environments.
  • Deep intuition for data quality, including the ability to reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training.
  • Experience building quality-control systems, evaluations, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure.
  • Comfort working across human and technical systems involving domain experts, vendors, generated data, model outputs, graders, and infrastructure.
  • Strong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences.

Responsibilities

  • Lead HUD’s data quality strategy, including building quality-control systems, defining and enforcing quality standards, and designing experiments to grade agent outputs.
  • Develop methods for validating synthetic data at scale, such as failure-mode analysis, task mutation checks, and trajectory auditing.
  • Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data-generation workflows.
  • Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops.
  • Build internal research judgment around what makes agent training data useful, rather than merely superficially correct.
  • Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed.

Desired Qualifications

  • Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration.
  • Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems.
  • Comfort designing metrics, experiments, and quality-assurance and quality-control processes, rather than only executing them.
  • Early-stage startup experience and ability to work independently in fast-paced environments.
  • Attention to detail and ability to spot subtle inconsistencies or edge cases in data.

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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