Research Engineer, Robotics Data

HUD is building infrastructure and marketplaces for reinforcement learning training data and evaluations for frontier AI agents. The Research Engineer will develop robotics datasets and evaluations for embodied AI, defining data specifications, building validation methods, and running experiments to assess how data quality and structure affect model performance.

Responsibilities:

• Research the data needs of robot learning and physical AI systems, and turn them into concrete dataset and evaluation specifications
• Define data schemas, annotations, ground truth, and quality standards across robotics data types
• Design collection and review protocols that external data providers can execute reliably
• Build tools and validation workflows to audit datasets, identify quality issues, and give providers actionable feedback
• Run experiments and analyze model behavior to understand how data quality, coverage, and structure affect performance
• Work with HUD’s research and engineering teams, and when needed with data vendors and buyers, to improve robotics data offerings

Requirements:

• Experience in robotics, robot learning, embodied AI, or closely related multimodal research
• Proficiency in Python and experience building data processing, analysis, or evaluation tools
• Experience turning research questions into dataset specifications, experiments, and measurable quality criteria
• Strong understanding of what makes robotics data useful for training or evaluation—and where it can be misleading
• Attention to detail and the ability to spot subtle errors, coverage gaps, and failure modes in complex data
• Experience building research tools or pipelines without a fully prescribed roadmap
• Worked with robot trajectories, demonstrations, video, sensor data, simulation, or other multimodal robotics datasets
• Experience with imitation learning, reinforcement learning, or vision-language-action models
• Worked in unstructured problem spaces and take ownership from early research through production deployment
• Early-stage startup experience and strong communication skills for collaboration across teams and time zones