Lead the data quality team at HUDHUD to build QC systems, validation methods, and experiments that measure and improve training data for frontier AI agents and RL environments. Requires deep data quality intuition, Python/Docker/Linux proficiency, and experience turning research insights into production evaluation pipelines.
Salary not listed
On-site7+ YOEML Engineering
About the role
Responsibilities
Lead HUD’s data quality strategy including building QC systems, defining and enforcing quality standards, and designing experiments to grade agent outputs
Develop new methods for validating synthetic data at scale, such as 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
Help build internal research taste around what makes agent training data actually useful, not just superficially correct
Mentor other research engineers to maintain a high bar for technical rigor, clarity, and execution speed
Requirements
Advanced proficiency in Python, Docker, and Linux environments
Deep intuition for data quality - you can reason about what makes tasks realistic, learnable, diverse, reliable, and useful for training
Experience building QC systems, evals, benchmarks, synthetic data pipelines, validation workflows, or model evaluation infrastructure
Comfort working across messy human and technical systems, including domain experts, vendors, generated data, model outputs, graders, and infrastructure
Strong written communication and the ability to explain methodology clearly to researchers, engineers, labs, and external audiences
Nice-to-Haves
Experience leading teams on ambiguous technical projects from problem definition through implementation and iteration
Experience working with subject-matter experts to capture domain judgment and convert it into scalable review or generation systems
Be comfortable designing metrics, experiments, and QA/QC processes, not just executing them
Early-stage startup experience with ability to work independently in fast-paced environments
Be detail-oriented and able to spot subtle inconsistencies or edge cases in data
Skills
PythonDockerLinuxqc systemsevalsbenchmarkssynthetic data pipelinesvalidation workflowsmodel evaluation infrastructure
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