hud (YC W25)

Research Engineer

• Research data needs of robot learning and physical AI systems and turn them into dataset and evaluation specifications
• Define data schemas, annotations, ground truth, and quality standards across robotics data types
• Design collection and review protocols for external data providers
• Build tools and validation workflows to audit datasets, identify quality issues, and provide actionable feedback
• Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance
• Collaborate with HUD’s research and engineering teams, data vendors, and buyers to improve robotics data offerings
• Participate in two technical interviews and a 2–3 day work trial during the hiring process

Research Engineer, Robotics Evals

Operations

• Own the human-in-the-loop layer of HUD’s operations across data suppliers, vendors, frontier labs, and enterprise buyers
• Participate in customer conversations when human involvement is needed to build trust, clarify context, or move work forward
• Resolve contract, onboarding, and platform blockers by coordinating with internal owners
• Convert recurring customer issues, feedback, and learnings into improved processes, platform functionality, and communications
• Build lightweight systems that streamline workflows and make future problem resolution easier
• Serve as the implementation counterpart to GTM Engineers
• Support successful customer activation and engagement

Customer Operations Lead

• Research data needs of robot learning and physical AI systems and turn them into dataset and evaluation specifications
• Define data schemas, annotations, ground truth, and quality standards across robotics data types
• Design collection and review protocols for external data providers
• Build tools and validation workflows to audit datasets, identify quality issues, and provide actionable feedback
• Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance
• Collaborate with HUD’s research and engineering teams, data vendors, and buyers to improve robotics data offerings
• Develop datasets and evaluations for training and evaluating embodied AI systems

Research Engineer, Robotics Data

Mechanical Engineer

• Design and build interfaces across HUD’s platform and marketplace, from early concepts through shipped product
• Make agent runs, evaluation results, reward signals, and quality feedback understandable and actionable
• Work with research and engineering to turn complex RL and evaluation workflows into useful tools
• Talk with users, observe where they struggle, and use insights to improve the product
• Develop thoughtful interaction patterns and maintain a high bar for usability, visual quality, and implementation
• Ship interfaces that improve through use and feedback

Design Engineer

Manager

• Lead research that makes HUD’s agent training data and evals more useful for improving frontier models
• Lead research engineers through ambiguous projects and turn findings into methods usable at scale
• Set research direction for data quality, including measuring whether tasks, trajectories, rewards, and evals are reliable and useful for training agents
• Lead research engineers from problem definition through experiments, implementation, and clear conclusions
• Coach research engineers to strengthen technical judgment and execution
• Design and review experiments connecting model behavior and failure modes to data, environment, and reward design
• Develop methods for validating and improving training data at scale, including trajectory audits, grader checks, and feedback loops
• Partner with research engineers, domain experts, and data vendors to turn research insights into better workflows, tools, and quality standards
• Communicate findings and tradeoffs clearly so the team can prioritize work and apply learning across research areas

Research Manager

Product Manager

• Own the marketplace product lifecycle from understanding vendor and buyer needs through prioritization, launch, and iteration
• Improve vendor onboarding, work submission, quality feedback, and offering management on HUD
• Improve research-team discovery, assessment, and access to environments and training data
• Collaborate with engineering, research, GTM, and operations to turn complex requirements and manual processes into reliable platform workflows
• Define and track marketplace health, product adoption, and user-experience measures
• Use user feedback and product data to prioritize future product work
• Work on HUD’s data marketplace for RL environments and training data

Product Manager, Marketplace

• Own the marketplace product lifecycle from understanding vendor and buyer needs through prioritization, launch, and iteration
• Improve vendor onboarding, work submission, quality feedback, and offering management on HUD
• Improve how research teams discover, assess, and access suitable environments and training data
• Collaborate with engineering, research, GTM, and operations to turn complex requirements and manual processes into reliable platform workflows
• Define and track marketplace health, product adoption, and user-experience measures
• Use user feedback and product data to prioritize future product development
• Participate in a process involving two technical interviews and a 2–3 day work trial

Product Manager – Platform

Account Manager

• Own HUD's outbound pipeline end-to-end, including targeting, enrichment, outreach, follow-up, and meeting booking
• Build and maintain GTM workflows such as outbound email sequencing, CRM automation, lead scoring, custom tooling, and inbound lead qualification
• Use AI tools to increase output, automate workflows, build scrapers, and create internal tools
• Develop and iterate messaging for technical buyers, including ML engineers, AI researchers, and engineering leaders
• Learn prospect businesses and industries and understand their pain points, workflows, and relevance of HUD's product
• Track pipeline metrics and optimize conversion at every stage
• Work closely with founders to develop and refine HUD's GTM playbook as the company scales
• Participate in a process involving two technical interviews and a 2–3 day work trial

GTM Lead, Commercial Partnerships

Business Development Rep

• Own HUD's outbound pipeline end-to-end, including targeting, enrichment, outreach, follow-up, and meeting booking
• Build and maintain GTM workflows such as outbound email sequencing, CRM automation, lead scoring, custom tooling, and inbound lead qualification
• Use AI tools to increase output, including building scrapers, automating workflows, and creating internal tools
• Develop and iterate messaging for technical buyers, including ML engineers, AI researchers, and engineering leaders
• Learn prospect businesses and industries and understand their pain points, workflows, and relevance of HUD's product
• Track pipeline metrics and optimize conversion at every stage
• Work closely with founders to develop and refine HUD's GTM playbook as the company scales
• Carry technical conversations and close deals at scale for HUD's RL training data, evaluation infrastructure, and marketplace

Business Development Lead

• Build systems to automate quality control for training data created using HUD’s infrastructure
• Create QC systems based on true understanding and human judgment, without heavy reliance on LLMs
• Define and enforce quality standards for training data
• Design experiments and metrics to grade agent outputs
• Partner with data vendors to debug quality issues and diagnose agent failure modes
• Provide actionable feedback and improve vendor data-generation processes
• Translate QC learnings into supplier-dataset auditing systems, including sampling strategies, validation pipelines, and feedback loops
• Integrate QC learnings into infrastructure tools and the data vendor portal to reduce anomalies, inconsistencies, and edge cases
• Participate in a process involving two technical interviews and a 2–3 day work trial

Research Engineer, QC Automation

Security Engineer

• Lead detection and incident response from signal to alert to postmortem
• Own HUD’s security roadmap across product security, cloud and infrastructure security, corporate security, incident response, and compliance
• Secure HUD’s APIs, platform, and data systems through threat modeling, design and code reviews, authentication and authorization controls, and secrets management
• Build monitoring, detection, and incident-response capabilities
• Lead investigations and postmortems and turn incidents and emerging threats into durable improvements
• Own SOC 2 and customer trust, including control design, security questionnaires, policy management, vendor reviews, and audits
• Partner with legal, commercial, engineering, and operations to translate customer contracts and data-license requirements into enforceable controls for data access, provenance, permitted use, retention, deletion, isolation, and auditability
• Secure HUD’s product, cloud infrastructure, data workflows, and internal systems as the first full-time security hire

Senior Security Engineer

• Build systems to detect PII, quasi-identifiers, credentials, and other sensitive information and design transformations based on data type and downstream use case
• Develop and benchmark detection approaches combining rules, statistical models, classifiers, and LLM-based methods
• Build production pipelines that anonymize raw data before downstream processing, training, evaluation, or synthetic data generation
• Create evaluation frameworks measuring privacy risk and retained data utility, including recall-weighted metrics, leakage tests, and adversarial re-identification attempts
• Design systems robust to new data sources, schema drift, unusual formats, and sensitive information embedded in unexpected fields
• Work with engineering, research, operations, and customers to translate privacy requirements into practical technical policies and safeguards
• Own the full pipeline for protecting privacy without destroying the structure and signal valuable for AI-agent training
• Participate in a 2–3 day work trial during the application process

Research Engineer, Privacy and Anonymization

Technical Program Manager

• Own data and evaluation programs end-to-end, from initial scoping and requirements gathering through production, quality assurance, delivery, and retrospective
• Translate ambiguous requests from AI labs and internal teams into clear specifications, including milestones, owners, dependencies, and acceptance criteria
• Maintain and improve data quality procedures using quantitative signals and qualitative inspection
• Analyze task coverage, difficulty, reward distributions, and related data-quality measures
• Maintain and improve delivery pipelines at scale
• Manage external vendors throughout the delivery lifecycle
• Improve workflows, documentation, automation, and cross-functional handoffs for data production and evaluation programs
• Partner with research, platform, and marketplace teams to translate learnings and feedback into product improvements

• Build high-quality benchmarks for evaluating frontier agents on domain-specific tasks
• Own the design, implementation, and quality of HUD’s internal agent benchmarks
• Work with subject-matter experts to define tasks and create domain-specific benchmarks evaluating realistic workflows
• Build infrastructure to reliably run models and agents against benchmark tasks
• Develop metrics and analyses to understand benchmark difficulty, reliability, and failure modes
• Validate whether benchmark performance correlates with real-world evaluations, customer needs, and lab expectations
• Write clear documentation and benchmark reports for technical audiences
• Participate in a 2–3 day work trial during the hiring process

Research Engineer – Benchmarks

• Build HUD’s synthetic data pipeline for RL training data and evals for frontier AI agents
• Work with subject-matter experts to create synthetic tasks across professional and technical domains
• Design methods that generate diverse, realistic, and learnable synthetic tasks
• Build systems and tooling to mutate, validate, and improve synthetic tasks
• Analyze model and agent performance to understand task learning outcomes and failure modes
• Develop metrics for synthetic task diversity, realism, and learnability
• Complete a process involving two technical interviews and a 2–3 day work trial