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HUD is building infrastructure for reinforcement learning training data and evaluations for frontier AI agents, plus a marketplace to sell these services to frontier labs. The company has raised $16M from top VCs and was part of Y Combinator W25.
As a Design Engineer, you will design and build user-facing experiences across HUD's products. Your work will help users create and evaluate environments, understand agent behavior, improve training data, and make informed decisions based on results. You'll focus on making technically dense workflows clear and actionable without oversimplifying critical details.
Key responsibilities include:
- Design and build interfaces across HUD's platform and marketplace, from concept through shipped product
- Make agent runs, evaluation results, reward signals, and quality feedback understandable and actionable
- Collaborate with research and engineering teams to transform complex RL and evaluation workflows into practical tools
- Conduct user research, observe pain points, and iterate on product improvements
- Develop thoughtful interaction patterns while maintaining high standards for usability, visual quality, and implementation
You'll work closely with users, researchers, and engineers to ship interfaces that improve through feedback and real-world use. The role requires both practical execution skills and strong product judgment about what to show, how to explain results, and where to give users control—decisions that will shape how humans interact with increasingly capable AI agents.
The team is ~25 people, mostly full-time and in-person, with some remote flexibility. The company includes 4 International Olympiad medalists, serial AI startup founders, and researchers with publications at top venues like ICLR and NeurIPS. HUD has 8 figures in funding and is scaling profitably to meet strong demand.
REQUIREMENTS:
You should have:
- Strong product and interaction design judgment, especially for complex tools or workflows
- Front-end engineering skills and ability to turn designs into working product interfaces
- Track record of shipping products, learning from users, and improving what you built
- Ability to understand technical concepts well enough to present them clearly to users
- Good judgment about when to ship quickly versus when an experience needs more care
- Clear communication and comfort working closely with engineers, researchers, and users
Strong candidates may also have:
- Experience designing developer tools, data-rich products, or research software
- Familiarity with AI agents, evaluations, reinforcement learning, or training data
- Experience creating reusable interface patterns as a product scales
- Experience working independently in early-stage companies
The company prioritizes technical aptitude and learning potential over years of experience and encourages motivated candidates to apply even if they don't meet all criteria.
