Technical Specialist, ML Data

Job description

Job responsibilities

• Drive the ML Flywheel: Lead the end-to-end lifecycle of ML data, from initial mining and curation to labeling policy definition, validation, and model evaluation.
• Translate Policy to Code: Lead the development of sophisticated labeling policies for complex AV domains (e.g., behavior prediction, long-tail edge cases).
• Build Evals & Metrics: Design and implement ML evaluation frameworks. Identify key data-centric drivers of model performance and create the metrics that track ML quality at the data level.
• Cross-Functional Leadership : Communicate effectively with technical and non-technical audiences at various levels of seniority, including producing analytical write-ups, dashboards, and data visualizations to convey…
• Influence ML data selection strategies (active learning, hard-mining) to ensure we are labeling the most impactful data to maximize ROI from the labeling effort

Job requirements

• 8+ years of experience in data analysis, including identifying trends, generating summary statistics, and drawing insights from quantitative and qualitative data.
• Deep understanding of the ML data lifecycle: labeling, taxonomy design, quality control, and data curation.
• Experience with ML Data Flywheel and working understanding of ML development life cycle (e.g., model deployment,model evaluation, data processing, debugging, fine tuning).
• Background in leading and managing complex programs that span across organizations and functions, with specific experience in Machine Learning data annotation or Human-in-the-Loop initiatives.
• Strong ability to thrive in a dynamic environment, demonstrating comfort and effectiveness when dealing with ambiguity.
• Ability to quickly learn and implement new concepts and utilize proprietary tools. Strong understanding of driving rules and regulations.