---
title: 'Forward Deployed Research Engineer at HUD'
canonical: 'https://feeny.ai/job/forward-deployed-research-engineer-hud-san-francisco-t766zptzg625'
type: 'job'
last_seen: '2026-09-08'
---

# Forward Deployed Research Engineer at HUD

- **Company:** HUD
- **Location:** San Francisco, CA
- **Employment:** full-time
- **Work type:** onsite
- **Posted:** 2026-07-13
- **Last confirmed live:** 2026-09-08
- **Apply:** https://jobs.ashbyhq.com/hud/4d61c93c-d17e-4ea1-9e17-85ca66a8db79

## Job description

## ABOUT HUD

HUD https://www.hud.ai/ is building infrastructure to create RL training data and evals for frontier AI agents, as well as a marketplace to sell these to frontier labs through the HUD marketplace. Our platform is used by frontier labs, Fortune 500 companies, and startups. We’ve raised $16M from top VCs and were YC W25.

## ABOUT THE ROLE

We’re looking for an Applied Research Engineer to own implementation. Our data buyers and sellers often have time-sensitive asks such as troubleshooting and running evals or cleaning data at scale. You’ll take the lead on diagnosing ambiguous technical problems and resolving them. This work is broad and hands-on - the right fit is a strong generalist AI engineer who can move quickly and wants to work closely with frontier AI labs and data vendors.

## RESPONSIBILITIES

- Own technical deployment requests from frontier AI labs, data vendors, and internal teams from triage to completion
- Ask the right questions to clarify ambiguous asks and identify what actually needs to be done
- Build tools and one-off pipelines to solve urgent customer or partner problems
- Coordinate with research and GTM teams to unblock deployments
- Balance speed and quality in situations where customers need fast turnaround and the path is not fully specified
- Document recurring issues and turn repeated manual work into reusable tools or processes

## EXPERIENCE

You may be a good fit if you have:

- Proficiency in Python, Docker, and Linux environments
- Experience working on benchmarks and evals - you can reason about what makes a task realistic, a rubric reliable, an environment usable, and a trajectory useful for RL training
- Strong debugging instincts across code, data, and environments
- Demonstrated ability to operate independently in ambiguous situations without a fully prescribed roadmap
- Strong judgment about when to move fast, when to escalate, and when correctness or security requires extra care
- Comfort working directly with technical customers, vendors, or cross-functional internal teams

Strong candidates may also have:

- Experience in applied research engineering or forward-deployed engineering
- Experience handling urgent production, customer, or deployment issues
- Early-stage startup experience with ability to work independently in fast-paced environments
- Strong communication skills for remote collaboration across time zones

We prioritize technical aptitude and learning potential over years of experience. Motivated candidates are encouraged to apply even if they don't meet all criteria.

## TEAM & COMPANY DETAILS

- Team Size: ~15 people currently, mostly full-time in-person, but some remote.
- Our team: Our team includes 4 International Olympiad medalists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS, etc.
- Company stage: We have 8 figures in funding and high revenue growth. We’re scaling profitably and quickly to meet very strong demand.

## LOGISTICS

- Employment: Full-time.
- Location: On-site only, for now. You can join the team in the San Francisco Bay Area or Singapore offices.
- Visa Sponsorship: We provide support for relocation and visas for strong full-time candidates to the US or Singapore.
- Timeline: Applications are rolling. The process is 2 technical interviews and a 2-3 day work trial.

## WHAT WE OFFER

- Competitive compensation
- 100% covered top-of-the-line medical, dental, and vision from Blue Shield of CA (US employees)
- Lunch and dinner when you’re in the office
- Company-wide holiday break (Christmas Eve to New Year’s Day) on top of PTO and paid holidays
- Other perks including an Equinox membership, 401k, and commuter benefits (US employees)
- Unlimited* access to tokens for ChatGPT, Claude Code, Cursor, etc. *By unlimited, we mean no one on our token usage leaderboard has ever hit a limit. So we have no idea what the limit is.

Due to high volume, we may not actively respond to every application, but feel free to contact us at recruiting@hud.so or elsewhere if we missed your application!

## About HUD

## Company Overview
- **One-liner**: HUD (Human Union Data, Inc.) provides the first comprehensive evaluation platform for Computer Use Agents (CUAs), enabling frontier AI labs to build reinforcement learning environments and run detailed evals for AI agents that browse the web.
- **Entity Type**: Private (YC W25 startup; raised $500K in Pre-Seed funding led by Y Combinator)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2025
- **Founders**: Jay Ram (CEO), Parth Patel (CTO)

## Core Business
- **Primary industry**: AI infrastructure – evaluation and reinforcement learning environments for browser‑based AI agents.
- **Target customers**: Frontier AI labs (B2B), researchers building computer‑use and browser‑use agents.
- **Mission / Purpose**: Provide the infrastructure to make AI agents work reliably in the real world by enabling detailed evals across thousands of tasks.

## Products & Services
- **[CUA Evals Platform](https://www.hud.so/)**: An all‑in‑one platform for evaluating computer‑use and browser‑use AI agents. Supports 40+ models (GPT, Kimi, Qwen, etc.) and includes hundreds of environments and thousands of tasks. Used to generate both evaluation results and training data for reinforcement learning.

## Market Standing
- **Valuation**: $3.3M (most recently disclosed, per Latka, Sep 2025)
- **Key Metric**: $1.1M revenue (2025) – note: Latka reports the company as bootstrapped, but other sources (YC, LinkedIn) show $500K in outside funding; this may reflect different accounting or a conflict in data.
- **Total Funding**: $500,000 (Pre-Seed, led by Y Combinator, with participation from several angel investors)
- **Notable Investors / Partners**: Y Combinator (lead), backed by YC W25 batch; works closely with frontier AI labs.
- **Growth Signals**:
  - Headcount grew ~150% YoY (from ~4 to ~9–15 employees).
  - Launched public beta in March 2026.
  - Active job postings increased 66.7% month‑over‑month.
  - Strong social engagement: LinkedIn followers grew 448.9% yearly.

## Competitive Advantages
- First‑mover in a nascent category: HUD’s platform is described as the first comprehensive evaluation tool specifically for Computer Use Agents.
- Tight integration with Y Combinator and frontier AI labs provides direct feedback and early‑adopter relationships.
- Supports a wide range of models (40+) and can generate RL training data, not just eval results.

## Strategic Focus
- Scale the platform to serve more frontier labs and researchers.
- Expand the number of environments and tasks covered by evaluations.
- Continue building out reinforcement learning infrastructure for agentic AI.

## Why Work Here
- **Culture**: Small, ambitious team (≈15 people) backed by Y Combinator, focused on solving a cutting‑edge problem in AI reliability.
- **Work Policy**: In‑office / in‑person in San Francisco (HQ). Some roles listed as hybrid.
- **Notable Perks**:
  - Competitive salary ranges (e.g., Platform Engineer: $140K – $250K; other roles $70K – $120K).
  - Opportunity to shape the infrastructure that leading AI labs depend on.
  - Fast‑paced startup environment with high ownership.

## Sources
1. [Y Combinator – HUD](https://www.ycombinator.com/companies/hud)
2. [LinkedIn – HUD](https://www.linkedin.com/company/hud-evals)
3. [Built In San Francisco – HUD](https://www.builtinsf.com/company/hud)
4. [Latka – HUD](https://getlatka.com/companies/www.hud.so)
5. [Hud Careers Page](https://jobs.ashbyhq.com/hud)

## Other roles at HUD

- [Legal Operations Lead](https://feeny.ai/job/legal-operations-lead-hud-san-francisco-r3g81avd0wqm) — San Francisco, CA
- [Full-Stack Software Engineer, Reinforcement Learning](https://feeny.ai/job/full-stack-software-engineer-reinforcement-learning-hud-san-francisco-j072pqy2sdyd) — San Francisco, CA
- [Lead Research Engineer, Data Quality](https://feeny.ai/job/lead-research-engineer-data-quality-hud-san-francisco-394mwe6veyfg) — San Francisco, CA
- [Research Engineer, Synthetic Data](https://feeny.ai/job/research-engineer-synthetic-data-hud-san-francisco-nr8xjzh0398z) — San Francisco, CA
- [Research Engineer, Benchmarks](https://feeny.ai/job/research-engineer-benchmarks-hud-san-francisco-wjf8abeym8d7) — San Francisco, CA
- [Research Engineer, QC Automation](https://feeny.ai/job/research-engineer-qc-automation-hud-san-francisco-w32xstbv187y) — San Francisco, CA
- [Recruiter](https://feeny.ai/job/recruiter-hud-san-francisco-xw2dz4wpr8ty) — San Francisco, CA
- [GTM Engineer](https://feeny.ai/job/gtm-engineer-hud-san-francisco-d7nyzfj2e34w) — San Francisco, CA
- [Growth Lead](https://feeny.ai/job/growth-lead-hud-san-francisco-3b9g4js2kfn7) — San Francisco, CA
- [Platform Engineer](https://feeny.ai/job/platform-engineer-hud-san-francisco-6xxa9scc9mqb) — San Francisco, CA