Teams at Torc Robotics

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.

Build and maintain MLOps pipelines that convert, compile, benchmark, validate, and release machine learning models for autonomous truck edge hardware. Maintain model release registries, ensure version traceability and reproducibility, compare latency and accuracy across platforms, and collaborate with perception and safety teams to meet strict production requirements.