HUD
About HUD
HUD (YC W25)
is developing agentic evals for Computer Use Agents (CUAs) that browse the web. Our
CUA Evals framework
is the first comprehensive evaluation tool for CUAs.
Read all the information about this opportunity carefully, then use the application button below to send your CV and application. Our Mission:
People don't actually know if AI agents are working. To make AI agents work in the real world, we need detailed evals for a huge range of tasks. We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation infrastructure at scale. About the role
We're looking for a
research engineer
to help build out task configs and environments for evaluation datasets on
HUD's CUA evaluation framework . Responsibilities
Build out environments for HUD's CUA evaluation datasets, including evals for safety redteaming, general business tasks, long-horizon agentic tasks etc.
Create custom CUA datasets/evaluation pipelines - likely later as we're focusing on existing evals for the short term.
Experience
Technical Skills Proficiency in Python, Docker, and Linux environments
React experience for frontend development
Production-level software development experience preferred
Strong technical aptitude and demonstrated problem-solving ability
You may be a good fit if you: Have hands-on experience with LLM evaluation frameworks and methodologies
Have contributed to evaluation harnesses (EleutherAI, Inspect, or similar)
Built custom evaluation pipelines or datasets
Worked with agentic or multimodal AI evaluation systems
We prioritise
contributions that show quality and quantity , such as building out large, high-quality eval datasets. Strong candidates may have: Startup experience in early-stage technology companies with ability to work independently in fast-paced environments
Strong communication skills for remote collaboration across time zones
Familiarity with current AI tools and LLM capabilities
Understanding of safety and alignment considerations in AI systems
Evidence of rapid learning and adaptability in technical environments
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 : ~5-10 people currently, looking to hire 2-3 additional people (though we judge case-by-case - could be zero or a lot more depending on candidates).
Our team:
Our team includes 4 international Olympiad medallists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS etc.
Logistics
Employment : Fulltime preferred, but we're willing to consider internship offers.
Location : Remote-friendly, but if you’re in the San Francisco Bay Area, we do have an office you can work together in. We do prefer applicants who can show up to meetings in Pacific Time (UTC-7:00/8:00) or China/Singapore Time (UTC +8:00).
Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates. For part-time/contract/internship arrangements, we'll work fully remote (which makes things simpler anyway).
Timeline : Applications are rolling. The process should involve 1-2 interviews and take less than a week.
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!
#J-18808-Ljbffr
HUD (YC W25)
is developing agentic evals for Computer Use Agents (CUAs) that browse the web. Our
CUA Evals framework
is the first comprehensive evaluation tool for CUAs.
Read all the information about this opportunity carefully, then use the application button below to send your CV and application. Our Mission:
People don't actually know if AI agents are working. To make AI agents work in the real world, we need detailed evals for a huge range of tasks. We're backed by Y Combinator, and work closely with frontier AI labs to provide agent evaluation infrastructure at scale. About the role
We're looking for a
research engineer
to help build out task configs and environments for evaluation datasets on
HUD's CUA evaluation framework . Responsibilities
Build out environments for HUD's CUA evaluation datasets, including evals for safety redteaming, general business tasks, long-horizon agentic tasks etc.
Create custom CUA datasets/evaluation pipelines - likely later as we're focusing on existing evals for the short term.
Experience
Technical Skills Proficiency in Python, Docker, and Linux environments
React experience for frontend development
Production-level software development experience preferred
Strong technical aptitude and demonstrated problem-solving ability
You may be a good fit if you: Have hands-on experience with LLM evaluation frameworks and methodologies
Have contributed to evaluation harnesses (EleutherAI, Inspect, or similar)
Built custom evaluation pipelines or datasets
Worked with agentic or multimodal AI evaluation systems
We prioritise
contributions that show quality and quantity , such as building out large, high-quality eval datasets. Strong candidates may have: Startup experience in early-stage technology companies with ability to work independently in fast-paced environments
Strong communication skills for remote collaboration across time zones
Familiarity with current AI tools and LLM capabilities
Understanding of safety and alignment considerations in AI systems
Evidence of rapid learning and adaptability in technical environments
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 : ~5-10 people currently, looking to hire 2-3 additional people (though we judge case-by-case - could be zero or a lot more depending on candidates).
Our team:
Our team includes 4 international Olympiad medallists (IOI, ILO, IPhO), serial AI startup founders, and researchers with publications at ICLR, NeurIPS etc.
Logistics
Employment : Fulltime preferred, but we're willing to consider internship offers.
Location : Remote-friendly, but if you’re in the San Francisco Bay Area, we do have an office you can work together in. We do prefer applicants who can show up to meetings in Pacific Time (UTC-7:00/8:00) or China/Singapore Time (UTC +8:00).
Visa Sponsorship : We provide support for relocation and visas for strong full-time candidates. For part-time/contract/internship arrangements, we'll work fully remote (which makes things simpler anyway).
Timeline : Applications are rolling. The process should involve 1-2 interviews and take less than a week.
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!
#J-18808-Ljbffr