Platform for building RL environments and agent evals
HUD builds reinforcement learning environments and evaluation platforms for AI agents, turning software, web apps, and interfaces into testable setups. It serves frontier AI labs, researchers, and enterprises to benchmark, train, and improve agent performance at scale. This infrastructure enables reliable real-world AI agent deployment through detailed telemetry and scalable evals.
Own the human-in-the-loop customer operations for an RL training data and evals platform. Resolve blockers, improve processes from feedback, and build lightweight systems as the implementation counterpart to GTM Engineers. Ideal for those with ops/consulting experience, strong judgment, and AI familiarity.
Build synthetic data pipelines and tasks to train frontier AI agents. Requires Python, Docker, Linux, and experience creating realistic, scalable synthetic training data for models and evals.
Build high-quality, domain-specific benchmarks and infrastructure to rigorously evaluate frontier AI agents on realistic workflows. Requires strong Python/Docker/Linux skills, experience with evals or benchmarks, and a deep understanding of what makes a benchmark reliable and useful.
Build QC automation systems for RL training data and agent evals at HUDHUD. Design quality standards, validation pipelines, experiments and metrics without heavy LLM reliance; partner with vendors to debug and improve data generation. Requires Python, Docker, Linux and experience building scalable QA/QC systems end-to-end.
Applied Research Engineer owning technical deployment requests, troubleshooting, building tools/pipelines, and resolving ambiguous problems for frontier AI labs and data vendors at HUDHUD. Requires strong Python/Docker/Linux skills, eval/benchmark experience, independent problem-solving in fast-paced ambiguous settings.
Recruiter to own end-to-end hiring for engineering, product, and GTM roles at an early-stage AI startup. Requires experience sourcing and evaluating technical talent, building recruiting processes and tooling, and strong product understanding to pitch candidates.
Build and own end-to-end outbound GTM pipeline for AI training data platform, including technical outreach to ML engineers and AI researchers, workflow automation with AI tools, and deal closing. Requires engineering background, GTM tooling expertise, and startup experience.
Head of Growth at HUD to evangelize frontier AI infrastructure vision by building networks with AI researchers/engineers, leading recruiting and culture definition, hosting events, and creating content. Ideal for extraverted networkers with startup experience who can learn recruiting, marketing, and GTM functions.
Platform Engineer owning reliability, scale, performance, and developer experience of core infrastructure and backend systems at HUD. Requires strong production AWS/Kubernetes experience plus backend engineering judgment on services, queues, databases, and APIs.
Owns marketing strategy, social media growth on Twitter/X and LinkedIn, content creation explaining technical AI products, and event planning/execution to build brand and pipeline for AI infrastructure startup. Requires startup marketing experience and technical communication skills.
Builds QA systems, tooling, and workflows to audit and validate large-scale RL training data from suppliers. Partners with vendors to improve data quality using Python, Docker, and AI/ML techniques for frontier AI infrastructure.