Teams at Torc Robotics

Reviews and audits high-definition map datasets for accuracy, completeness, and safety-critical defects. Performs quality control on geospatial data derived from LiDAR, imagery, and vehicle sensors; documents and triages issues; defines QC metrics, sampling strategies, and repeatable workflows; collaborates with mapping engineers, autonomy teams, and vendors; analyzes defect trends; maintains procedures; and supports launch readiness for new corridors and operational domains.

Provide on-call MLOps support for machine learning teams by triaging pipeline issues, debugging workflows, identifying root causes, and maintaining reliable model development operations. Collaborate with senior engineers to improve tooling, documentation, and processes, while communicating technical findings across teams. The role requires Python, foundational PyTorch and model pipeline experience, plus familiarity with CI/CD, monitoring, logging, and orchestration tools.

Lead cloud cost management and FinOps activities across multi-cloud environments: cost analysis, budgeting, forecasting, tagging and allocation, rate optimization, anomaly/root-cause analysis, billing data aggregation, dashboards and showback/chargeback models, and cross-functional cost governance using FinOps best practices and Lean-Agile methods.