Labeling Operations Manager

February 11

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Logo of Stack AV

Stack AV

Stack AV is a company that is revolutionizing the transportation industry through its autonomous trucking solutions, driven by advanced artificial intelligence. The company focuses on developing AI-powered autonomous systems to enhance safety, reliability, and efficiency in trucking operations. Stack AV is committed to addressing the challenges of the trucking industry by designing smart solutions to improve supply chain intelligence, business outcomes, and delivery speed. Safety is a core principle, and the company leverages cutting-edge AI, machine learning, and cloud technologies to innovate within the industry.

πŸ“‹ Description

β€’ The Labeling Operations team is responsible for ensuring a high throughput of quality labels of sensor data used in the development of our Self Driving System. β€’ As the Labeling Operations Manager you will manage a metrics driven internal team as well as external vendors responsible for delivering the labels needed by internal customer teams who rely on this data for the development of critical ML models used in Autonomy. β€’ You will also partner closely with Product and Engineering teams to identify and define requirements for efficient tooling and automation to streamline labeling and QA processes. β€’ This is a critical role to the development and validation of our Autonomy System and will involve close partnership and collaboration broadly across Stack to serve the needs of the organization.

🎯 Requirements

β€’ 10+ years of Operations Management or related experience β€’ Significant experience leading teams responsible for data annotation or related functions β€’ Demonstrated experience managing globally distributed workforce vendors β€’ Proven ability to define and implement complex operational workflows that are heavily data driven β€’ Strong data analysis skills including experience building and reporting on meaningful business metrics β€’ Prior experience working in an AV or Robotics company is strongly preferred β€’ General understanding of ML training of Perception models and associated inputs (e.g. calibration), data infrastructure, and visualization tooling preferred

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