Jobs at hud

Design and build interfaces for HUD’s AI agent training, evaluation, and marketplace products. Translate complex reinforcement learning workflows, agent runs, evaluation results, reward signals, and quality feedback into clear, actionable experiences. Collaborate with users, researchers, and engineers to ship polished products, conduct user discovery, improve usability, and develop reusable interaction patterns.

Own the HUD data marketplace product lifecycle, improving vendor onboarding, submission workflows, quality feedback, and marketplace management. Enable research teams to discover, evaluate, and access AI training data and RL environments. Partner with engineering, research, GTM, and operations to simplify complex workflows, define marketplace health metrics, and prioritize product improvements using user feedback and quantitative data.

Lead research on improving AI agent training data and evaluations. Manage and mentor research engineers, define research direction, design rigorous experiments, validate data quality, diagnose reward and grading failures, and translate findings into scalable tools, workflows, and standards. Collaborate with engineers, domain experts, and vendors while communicating technical conclusions and tradeoffs clearly.

Own the data marketplace product lifecycle, improving vendor onboarding, submissions, quality feedback, offerings management, and buyer discovery. Partner with engineering, research, GTM, and operations to convert complex requirements into reliable workflows. Define marketplace health and adoption metrics, use qualitative and quantitative insights to prioritize product improvements, and guide launches and iteration in a fast-growing AI startup.

Business Operations generalist supporting hiring, people operations, finance, vendors, cross-functional projects, content, events, and special initiatives. The role partners closely with founders and the Chief of Staff, builds scalable operating systems, handles ambiguous business questions, and uses AI to automate workflows. It is an early-career, fast-paced position at an AI infrastructure startup requiring strong organization, communication, judgment, and adaptability.

Lead end-to-end recruiting for engineering and go-to-market roles at an early-stage AI infrastructure startup. Responsibilities include building recruiting operations and tooling, partnering with founders on role and interview design, sourcing exceptional candidates, evaluating technical and product-market aptitude, supporting onboarding, and helping establish performance reviews, leveling, and feedback systems.

Own HUD’s outbound commercial pipeline end to end, including targeting, enrichment, outreach, qualification, follow-up, and meeting booking. Build scalable GTM workflows, CRM automations, lead scoring, and custom AI-powered tools. Develop messaging for ML engineers, AI researchers, and engineering leaders, close deals, analyze funnel performance, and collaborate with founders to refine the GTM playbook.

Own HUD’s outbound business development pipeline from targeting and enrichment through outreach, follow-up, qualification, and meeting booking. Build scalable GTM workflows, CRM automation, lead scoring systems, and custom AI-powered tools. Develop technical messaging for ML engineers, AI researchers, and engineering leaders, track pipeline metrics, optimize conversions, and collaborate with founders to refine the GTM playbook. The role also involves closing deals and rapidly learning prospect industries and technical workflows.

HUD is accepting applications for exceptional candidates across research, engineering, go-to-market, and operations. Applicants must propose a specific contribution based on research into HUD and provide concrete evidence of exceptional ability and relevant experience. The role is full-time, based in San Francisco, Singapore, or remotely with substantial time-zone overlap. The hiring process includes two technical interviews and a two- to three-day work trial.

Build privacy and anonymization systems that detect and transform PII, credentials, secrets, and other sensitive information in data used for AI training. Develop detection methods combining rules, statistical models, classifiers, and LLMs; create production data-processing pipelines; evaluate privacy risk and retained utility; and ensure robustness against schema drift, unusual formats, leakage, and adversarial re-identification.

Own end-to-end technical data and evaluation programs for frontier AI labs, coordinating requirements, milestones, vendors, quality assurance, delivery, and retrospectives. Translate ambiguous technical needs into executable plans, improve data quality and production workflows, manage cross-functional dependencies, and partner with research, platform, and marketplace teams to inform product improvements.

Own HUD’s production infrastructure and backend platform systems, focusing on reliability, scalability, performance, cost, deployment safety, and developer experience. Build AWS infrastructure with Terraform and Kubernetes, improve CI/CD and observability, design resilient services and asynchronous systems, manage incidents, and develop internal automation and tooling. The role supports high-availability AI, marketplace, and data-heavy workloads.

Own HUD’s legal operations infrastructure, including intake, contracting workflows, approvals, document management, templates, playbooks, automation, and outside-counsel coordination. Draft, review, and negotiate commercial technology agreements such as MSAs, SOWs, data licenses, SLAs, NDAs, and vendor contracts. Improve legal scalability by standardizing routine work, managing risk, controlling legal spend, and escalating specialized matters appropriately.

Lead HUD's data quality strategy for RL training data: build QC systems, validate synthetic data at scale, design evaluation experiments, partner with researchers and vendors, convert research insights into production validation pipelines and tools, and mentor research engineers.

Build full-stack product surfaces, backend services, APIs, databases, dashboards, internal tools, vendor workflows, and observability for reinforcement learning data and evaluation systems. Develop interfaces for browsing environments, inspecting trajectories, reviewing quality, debugging failures, and understanding model behavior. Partner with research, operations, vendors, and go-to-market teams to translate ambiguous requirements into reliable products. The role also involves production debugging and cloud infrastructure collaboration.

Tell HUD's story and evangelize vision; source and meet candidates, AI engineers, and ecosystem partners; define culture, screen and onboard hires; build recruiting ops and tooling; run events and content; collaborate with marketing, Chief of Staff, and GTM engineers to drive introductions and community growth.

Build scalable QC systems and validation pipelines for RL training data: define quality standards, design metrics and experiments, audit supplier datasets, partner with vendors to diagnose failures, and integrate learnings into infrastructure and vendor tooling.

Design, implement, and maintain domain-specific benchmarks and infrastructure to run agents/models. Define tasks with subject experts, develop metrics and analyses, validate benchmark relevance to real-world evals, and produce clear technical documentation and reports.

The recruiter will manage end-to-end recruiting for technical and go-to-market roles, optimizing processes and collaborating with team leads while sourcing top candidates and supporting new hire onboarding.

The GTM Engineer is responsible for managing HUD's outbound sales pipeline, optimizing conversion metrics, and developing GTM workflows, targeting technical buyers. The role requires technical aptitude and sales skills relevant to AI and ML domains.