hud (YC W25)

Infrastructure Engineer

• Own production uptime, latency, provisioning speed, infrastructure cost, and incident response for core platform services
• Build and maintain AWS infrastructure using Terraform, Kubernetes/EKS, Helm, Docker, EC2, CodeBuild, ECR, S3, IAM, networking, and secrets management
• Design and improve backend and platform systems for scale, including capacity planning, autoscaling, queueing, backpressure, cleanup jobs, retries, and rollback paths
• Define and improve dashboards, alerts, logs, traces, SLOs, runbooks, and on-call workflows
• Build reliable CI/CD, release automation, environment management, and deployment workflows
• Write code to automate systems, improve backend services, and create internal tooling
• Support HUD’s infrastructure for RL training data, evaluation systems, and its marketplace for frontier AI labs

Senior Software Engineer, Infrastructure

Data Engineer

• Lead HUD’s data quality strategy, including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
• Develop methods for validating synthetic data at scale, including failure-mode analysis, task mutation checks, and trajectory auditing
• Partner with research engineers, domain experts, and data vendors to diagnose quality issues and improve data generation workflows
• Turn qualitative research insights into production systems, internal tools, dashboards, validation pipelines, and feedback loops
• Build internal research taste around what makes agent training data useful, not just superficially correct
• Mentor research engineers to maintain high standards for technical rigor, clarity, and execution speed
• Lead the data quality team in evaluating thousands of tasks across RL environments, synthetic data, benchmarks, and domain-specific workflows

Lead Research Engineer, Data Quality

Full-stack Engineer

• Build product surfaces, backend systems, and internal tools powering HUD’s reinforcement-learning data engine
• Own product surfaces end-to-end, including backend services, APIs, databases, dashboards, tools, vendor workflows, data collection, and observability for RL rollouts
• Develop tools for browsing environments, inspecting trajectories, reviewing task quality, debugging failures, and understanding model behavior
• Build vendor-facing workflows for creating, submitting, testing, and iterating on RL environments and training data
• Create dashboards and observability tools for environment quality, eval results, data collection progress, grader issues, reward signal problems, and pipeline health
• Design backend services and APIs connecting task authoring, data collection, evaluation, QA/QC, and RL training infrastructure
• Partner with research, operations, and go-to-market teams to turn ambiguous requests into well-designed systems
• Participate in two technical interviews and a 2–3 day work trial as part of the hiring process

Full-Stack Software Engineer, Reinforcement Learning

Research Engineer

• Own technical deployment requests from frontier AI labs, data vendors, and internal teams from triage to completion
• Clarify ambiguous requests and identify the actual work required
• Build tools and one-off pipelines for urgent customer or partner problems
• Coordinate with research and go-to-market teams to unblock deployments
• Balance speed and quality when customers need fast turnaround and requirements are not fully specified
• Document recurring issues and convert repeated manual work into reusable tools or processes
• Work closely with frontier AI labs and data vendors
• Complete a 2–3 day work trial as part of the hiring process

Senior Software Engineer, Infrastructure

Lead Research Engineer, Data Quality

Full-Stack Software Engineer, Reinforcement Learning

Forward Deployed Research Engineer