Top Tech Jobs & Startup Jobs in Boston, MA

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 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.