The Research Engineer will develop datasets and evaluation specifications to improve the training and performance of embodied AI systems. They will design validation protocols, build data pipelines, and conduct experiments to ensure high-quality robotics data.
The Customer Operations Lead will manage the human-in-the-loop layer of operations, ensuring successful customer activation and engagement. They will resolve platform and contract blockers while turning recurring feedback into improved internal processes and systems.
You will build full-stack tools and backend services to assess and improve training data and evaluation workflows for frontier AI agents. This involves creating dashboards, metrics, and interfaces that help the quality team identify, understand, and resolve data quality issues.
You will collaborate with cross-functional teams to identify friction in RL environment workflows and translate those findings into clear product priorities. You will own the end-to-end design, prototyping, and shipping of product improvements across the platform and marketplace.
You will research and define data specifications for robotics and embodied AI systems while building validation workflows to ensure high-quality training data. Additionally, you will run experiments to analyze how data structure and quality impact model performance.
Design and build intuitive interfaces for HUD's platform to manage complex RL and evaluation workflows. Collaborate closely with research and engineering teams to turn technical agent data into actionable user experiences.
You will own the marketplace product lifecycle, from understanding user needs to prioritization, launch, and iteration. You will collaborate with engineering, research, and GTM teams to transform complex requirements into reliable platform workflows.
You will own the product lifecycle for the data marketplace, managing vendor onboarding and research team discovery workflows. You will collaborate with engineering, research, and GTM teams to translate complex requirements into reliable platform features.
The Research Manager will lead research initiatives to improve the quality and utility of agent training data and evaluation frameworks. They will guide research engineers through the full project lifecycle, from problem definition and experimental design to implementation and scaling.
The GTM Lead will own the end-to-end outbound pipeline, including targeting, enrichment, and outreach to technical buyers. They will also build and maintain GTM workflows and iterate on messaging to effectively scale the company's commercial partnerships.
You will own the end-to-end outbound pipeline, including targeting, outreach, and meeting booking for technical buyers. Additionally, you will build and maintain GTM workflows and iterate on messaging to effectively reach ML engineers and AI researchers.
You will build and maintain robust pipelines to detect and remove sensitive information from raw data to ensure it is safe for AI training. Additionally, you will design evaluation frameworks to measure privacy risks and data utility while ensuring systems remain resilient to schema drift and adversarial threats.
You will own the reliability, scale, and performance of core infrastructure while building and maintaining AWS systems using Terraform and Kubernetes. Additionally, you will define observability workflows and CI/CD pipelines to ensure high availability and developer productivity.
You will own end-to-end data and evaluation programs, translating ambiguous technical requirements into clear, executable specifications. Additionally, you will manage external vendors and streamline internal workflows to ensure high-quality data delivery at scale.
Candidates are expected to define their own contributions to help HUD grow and demonstrate how their specific skills align with company goals. You must conduct thorough research on HUD's work and pitch your value proposition to the team.
