Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

Leads a team responsible for simulation-based verification and validation of autonomous trucks. Defines V&V strategy, scenario coverage, test campaigns, toolchain requirements, and safety-case evidence aligned with automotive standards. Partners with product, systems, safety, engineering, tools teams, and vendors to establish release criteria, identify testing gaps, analyze results, and report risks and progress to leadership. The role also involves team development, simulation scenario management, and advancing autonomous vehicle validation practices.

Leads vehicle-level verification and validation for L4 autonomous trucks. Defines test strategies, develops scenario-based test coverage, analyzes ground-truth and onboard vehicle data, and manages test platforms and toolchains. Builds and mentors test engineering teams, oversees budgets and schedules, prioritizes safety risks, and communicates results to leadership. The role also supports safety cases, regulatory and customer audits, software tool qualification, and compliance with automotive safety standards.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

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.

Design, implement, test, and maintain scalable C++ software for autonomous vehicle and perception systems. Work across full SDLC, support vehicle testing, simulation-based regression, data acquisition/analysis from remote vehicles, root-cause analysis, and mentor engineers while collaborating with ML researchers and cross-functional teams.

Develop and deploy machine learning solutions for autonomous truck applications: EDA, deep learning model development, embedded deployment, data ingestion/curation, analytics, visualization, technical leadership, and process improvement across manufacturing and business domains.