Jobs at hud

Conduct daily outbound prospecting through cold calls, emails, and LinkedIn messages. Identify contacts, explain HUD’s data-listing platform, handle objections, follow up consistently, and guide providers through signup and listing submission. Maintain accurate contact records, meet outreach targets, and generate complete listings aligned with AI buyer demand. The role is Singapore-based, full-time, and offers training and support for early-career candidates.

Conduct daily cold calls, emails, and LinkedIn outreach to prospective data providers. Explain HUD’s marketplace, address objections, follow up consistently, and guide interested providers through signup and listing submission. Maintain accurate contact records, collect complete listing information, assist prospects, and meet outreach and listing targets. This is an early-career role emphasizing communication, persistence, organization, sales-tool proficiency, and learning potential.

Design and build interfaces across HUD’s AI data platform and marketplace. Translate complex agent runs, evaluation results, reward signals, and reinforcement learning workflows into clear, actionable products. Partner with users, researchers, and engineers; conduct user research; ship and refine experiences; establish reusable interaction patterns; and maintain high standards for usability, visual quality, and implementation.

Build reliable software and infrastructure for frontier AI agent research, including environment execution, trajectory collection, evaluations, experiment orchestration, data pipelines, and observability. Partner with research engineers to turn prototypes into reusable, tested systems; improve reproducibility, scalability, cost efficiency, and recovery from failed runs; and own infrastructure from design through operation.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Develop datasets, evaluation methods, validation tools, and quality standards for robotics and embodied AI systems. Translate research needs into dataset specifications, design collection protocols, audit multimodal robotics data, and run experiments analyzing how data quality and structure affect model performance. Collaborate with research, engineering, data providers, and customers to improve robotics data offerings.

Own customer-facing operations across data suppliers and AI labs, including activation, engagement, onboarding, contract and platform issue resolution. Build trust through customer conversations, coordinate internal blockers, and convert recurring feedback into improved processes, platform capabilities, communications, and lightweight workflow systems. The role requires strong judgment, organization, follow-through, CRM familiarity, and comfort working independently in a fast-paced startup environment.

Build and improve HUD’s platform and marketplace for RL training data and evaluations. The Product Engineer will identify user friction, define priorities, design prototypes, and ship full-stack product experiences. Responsibilities include improving agent-run and evaluation workflows, building frontend and backend functionality for onboarding and environment submission, and using user feedback and product signals to iterate. The role requires close collaboration with product, design, engineering, research, and go-to-market teams.

Develop robotics datasets and evaluations for embodied AI systems. Responsibilities include defining data schemas, annotations, quality standards, collection protocols, validation tools, and review workflows. The role also involves running experiments to assess how data quality and structure affect model performance, collaborating with research, engineering, vendors, and customers, and taking research tools from conception through deployment.

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.

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 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.

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.