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HUD

AI agent evaluation platform and benchmarks

Lead Research Engineer - Data Quality

Full-TimeUpdated on 10/4/2026
$120k - $295k/yr
Senior
San Francisco, CA, USA
HybridRemote candidates must work hours with 70–80% overlap with either San Francisco or Singapore time zones.
H1B Sponsorship Available

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, including 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.
  • Help build internal research judgment about what makes agent-training data useful, not just 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/quality-control processes, not just executing them.
  • Early-stage startup experience and the ability to work independently in fast-paced environments.
  • Attention to detail and the ability to spot subtle inconsistencies or edge cases in data.

About the company

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, California

Founded

2025

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Simplify's Take

What believers are saying

  • HUD says over 50 businesses use its platform as of June 2026.
  • HUD reported 1.3M task runs and millions monthly in marketplace volume.
  • Thirty open roles on October 5, 2026 signal aggressive hiring across research and GTM.

What critics are saying

  • OpenAI, Browserbase, and model labs can internalize HUD’s eval stack by 2027.
  • HUD’s marketplace depends on vendor liquidity; thin supply kills pricing power fast.
  • If frontier labs standardize one benchmark suite, HUD becomes a replaceable tooling layer.

What makes HUD unique

  • HUD raised a $16M Series A led by Standard Capital on June 30, 2026.
  • HUD combines RL environment SDKs, evals, and DataVendor marketplace across computer-use tasks.
  • HUD’s benchmarks already include Autonomy-10, SheetBench-50, OSWorld-Verified, and Assemble Bench.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

Paid Vacation

Paid Holidays

Commuter Benefits

Growth & Insights

Headcount

6 month growth

↓ -17%

1 year growth

↓ -17%

2 year growth

↓ -17%