Sr. Software Analyst, Annotation Analytics, Autonomy

About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations.

Summary In this role, you will analyze large-scale 2D/3D annotation datasets to identify data gaps, quality issues, and operational inefficiencies, and build dashboards and metrics that monitor annotation throughput, quality, cost, and vendor performance. You will partner closely with Annotation Operations, Perception, and Engineering teams to ensure data readiness for model training and evaluation. The ideal candidate is data-driven, detail-oriented, and execution-focused, with a strong understanding of annotation workflows and a passion for improving data quality and efficiency at scale. This role plays a critical part in enabling informed decision-making, continuous process improvement, and scalable annotation operations across internal teams and external vendors Responsibilities Data Analysis, Insights & Dashboards ● Analyze large-scale 2D/3D annotation datasets to identify data gaps, quality issues, coverage gaps, and inefficiencies impacting Perception and ML training. ● Design, build, and maintain dashboards and reports to monitor: ○ Annotation throughput, efficiency, and cost ○ Quality metrics (QA pass rates, error types, rework) ○ Vendor and annotator performance trends ● Translate complex annotation and perception data into clear, actionable insights for engineering, annotation operations, and leadership. ● Partner with Perception teams to align data readiness metrics with model training and evaluation needs. Annotation Operations & Quality ● Assess level of effort for annotation projects and support end-to-end execution, including in-house vs. vendor decisions. ● Collaborate with stakeholders to gather labeling requirements and define, maintain, and evolve labeling policies. ● Define and track annotation quality, productivity, and certification metrics; continuously refine benchmarks. ● Conduct QA/QC analysis on annotated data, identify systemic issues, and provide structured feedback to annotators and vendors. ● Implement and measure labeling efficiency improvements, using data to validate impact. Process Improvement & Automation ● Identify opportunities to make data delivery and annotation workflows faster, more accurate, and scalable. ● Build lightweight automation (scripts, queries, data pipelines) to reduce manual reporting and operational overhead. ● Maintain structured datasets (databases, tables, metrics pipelines) to enable consistent reporting and historical analysis. Cross-Functional & Vendor Collaboration ● Work closely with Annotation Ops, Perception, and Engineering teams to ensure consistent process implementation. ● Support vendor management by analyzing performance, cost, and quality metrics across multiple annotation partners. ● Prepare technical and operational reports to support planning, execution, and decision-making. ● Coordinate across teams and time zones to ensure alignment and timely delivery.

Required ● BS degree in a technical or related field with 2+ years of relevant experience (or 5+ years equivalent industry experience). ● 2+ years supporting data annotation, ML data, or labeling-related projects. ● Strong understanding of 2D/3D data annotation workflows for ML and Perception use cases. ● Hands-on experience with data analysis, metrics tracking, and dashboarding. ● Working knowledge of Python, SQL, databases, and/or data visualization tools. ● Experience analyzing LiDAR point clouds, video, and