Jobs at hud

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.

Build and optimize HUD's comprehensive evaluation framework for Computer Use Agents (CUAs) at scale, ensuring high performance and reliability.

Own reliability, scale, performance, and developer experience for HUDs core infrastructure. Build and maintain AWS/Terraform/Kubernetes-based systems, CI/CD, observability, and deployment workflows. Drive incident response, cost optimization, autoscaling, and backend/design improvements; write automation and internal tools to improve reliability and developer productivity.

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 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.

Lead implementation and deployments for frontier AI labs and data vendors: triage technical requests, build tools and one-off pipelines, diagnose ambiguous problems across code/data/environments, coordinate cross-functionally, and turn recurring manual work into reusable processes.

Research Engineers will build QA systems for training data, define quality standards, audit datasets, and collaborate with data vendors to enhance data generation processes.