Senior Quality and Process Manager

Genesis•San Carlos, CA

11d

About The Position

Genesis is a full-stack general-purpose robot company with a mission to make general-purpose robots a reality, unlocking infinite physical labor for the world and freeing people for creativity, exploration, and the pursuits they love. The company has unveiled Eno, its wheeled, dexterous robot powered by its GENE foundation model, and is beginning to deploy it with customers. This role is crucial for ensuring the quality of the data used to train these robots, which is essential for maintaining the pace of development. The position involves defining data quality standards, building the necessary QA systems, and engineering standardized workflows to ensure quality scales with the company's global operations. The role will focus on key metrics such as accept-rate, first-pass yield, rework, and cost per accepted hour, directly linking them to model outcomes. The goal is to establish a world-class operational standard for data collection and quality assurance.

Requirements

Experience owning data quality and process in a significant operation.
Five to ten years of experience leading data, quality, or operational teams.
Experience setting and raising quality standards in a frontier robotics lab, embodied-AI company, or top data/labeling operation.
Demonstrated ability to diagnose quality issues from data and implement corrective actions.
Proficiency in standard work, root-cause analysis, and continuous improvement methodologies.
Experience building a quality function and acceptance standard from scratch.
Ability to maintain a high-quality bar without becoming a bottleneck.
Six Sigma or equivalent is a plus.
SQL or Python is a plus.

Nice To Haves

Robotics or sensor-data fluency.
Experience with robotics, autonomy, or sensor-derived data.

Responsibilities

Own the data-quality metrics, including accuracy, consistency, rework rate, first-pass yield, and accept-rate for all lanes, and report them to leadership.
Establish a written, versioned acceptance standard and a defect taxonomy for all sites to use for grading data.
Build and implement the QA and audit system, including the review pipeline, audit process, and escalation workflow to identify and address problems on the floor and during annotation.
Ensure rapid feedback turnaround to operators, enabling behavioral changes within the same week and updating guidelines based on edge cases.
Engineer and standardize the data collection process to create documented, repeatable work that facilitates faster ramp-up at each site and station.
Act as the primary point of contact between collection, QA, and the model team, translating model requirements into clear acceptance criteria.