Senior Manager - AV Evaluation

General Motors•Sunnyvale, CA

1d•$296,300 - $453,900•Hybrid

About The Position

The Evaluation team builds and evolves the evaluation ecosystem that powers the development and scaling of GM’s autonomous driving technology. We develop metrics, evaluation infrastructure, automated workflows, and analysis approaches that enable data-driven decisions across AV development and verification. Partnering with Autonomy, Simulation, Systems, and Safety teams, we act as system-level integrators and arbiters of end-to-end AV quality. We own large-scale test scenario libraries, continuous evaluation pipelines, and critical risk assessment and release-gating components, treating road testing, data mining, training, and metrics as first-class use cases in a unified analytics framework. By joining this team, you will help shape GM’s core evaluation platforms and infrastructure, turn system-level results into clear feedback for engineering and leadership, and help accelerate validated AV deployment at scale. We are looking for a Senior Manager to lead a critical autonomy evaluation area spanning platform, methodology, and organizational execution. This leader will combine deep technical judgment, strong people leadership, strategic planning, and cross-functional influence to define how evaluation supports product, safety, and release decisions across GM’s autonomous driving programs.

Requirements

8+ years of relevant experience in robotics, autonomous systems software, data analysis, ML evaluation, autonomy analytics, or large-scale data/infrastructure platforms with a track record of delivering complex software or data systems.
5+ years of people leadership experience in complex engineering organizations, including leading managers or senior technical leaders.
Demonstrated ownership of a distinct technical or business area, including strategy, roadmap, execution planning, KPI management, and cross-functional outcomes.
Demonstrated technical leadership, including driving architectural decisions, influencing cross-team designs, and owning complex services or platforms end-to-end.
Experience building or leading production infrastructure for large-scale data processing, workflow orchestration, observability, and service reliability.
Experience developing Python/C++ in production team environments, including testing, performance, and code review.
Experience evaluating dynamic systems using numerical, statistical, and/or machine learning methods, including time-series data, state derivatives, dynamics, and interconnected subsystems.
Strong analytical curiosity and a disciplined approach to root-causing anomalous data and system discrepancies.
Strong ability to translate ambiguous evaluation needs and complex technical findings into clear plans, decisions, and communication for executive, engineering, and cross-functional audiences.
Bachelor’s, Master’s, or PhD in Computer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.

Nice To Haves

Experience in autonomous driving or field robotics, including interpreting results from simulation and field experiments.
Experience evaluating robotics or AV systems using sensor data such as camera, lidar, and radar, and working with large-scale time-series analysis.
Strong intuition for data visualization and the ability to turn high-dimensional metrics into clear, trustworthy views for technical and non-technical audiences.
Familiarity with statistical modeling, experimental design, and hypothesis testing for autonomy or simulation evaluation.
Proficiency in SQL and experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.
Experience with ROS or similar robotics/IPC frameworks, log pipelines, experiment databases, platform services, or evaluation infrastructure.
Prior experience with computational geometry, linear algebra, PyTorch, and ML techniques applied to perception, prediction, planning, or control.
Background contributing to release gating, risk assessment, and safety-related decisions for autonomy systems.
Experience using AI-assisted development and analytics tools to improve engineering productivity and evaluation coverage.

Responsibilities

Own the strategy, roadmap, and operating plan for a distinct Autonomy Evaluation business area spanning methodology, platforms, and infrastructure ensuring alignment with autonomy product goals, safety priorities, and release needs.
Define and evolve the end-to-end evaluation strategy for autonomy performance, including scenario coverage, behavioral quality measurement, trend detection, risk assessment, and release-gating readiness.
Drive cross-functional alignment with Autonomy, Simulation, Systems, and Safety organizations to translate ambiguous validation needs into clear priorities, roadmaps, and execution plans.
Oversee evaluation approaches for ML components across perception, prediction, and planning, ensuring methods are explainable, scalable, and useful to development and verification teams.
Guide the team in developing new statistical and machine learning methods to quantify performance and identify behavior patterns across scenes and operational domains.
Help the organization leverage emerging AI techniques, including VLMs and LLMs where appropriate, to classify autonomy performance and prioritize validation work with human-in-the-loop review.
Maintain a high technical bar through strong system design, code review, testing, observability, and engineering best practices.
Drive the development of scalable evaluation infrastructure, including data pipelines, workflow orchestration, developer tooling, and service interfaces that improve reliability and iteration speed.
Translate system-level results and platform health signals into clear feedback for engineering and leadership and drive execution against high-priority evaluation outcomes.
Build and lead a high-performing, inclusive engineering organization through hiring, manager development, coaching, and feedback.
Lead through managers and senior technical leaders, creating strong accountability, healthy operating rhythms, and clear ownership across teams.
Create clarity across priorities, roadmaps, dependencies, and staffing plans for a technically complex, cross-functional domain.
Establish org-level goals, KPIs, and success measures for evaluation quality, scalability, insight generation, and execution effectiveness.
Guide investment and resource decisions across near-term delivery and long-term platform capabilities, balancing immediate program needs with durable infrastructure and methodology improvements.
Raise the quality of execution through strong planning discipline, design reviews, metrics, and decision-making mechanisms.
Resolve complex cross-team conflicts and align stakeholders to drive progress on high-priority issues.
Collaborate with senior leadership to shape long-term strategy for autonomy evaluation and influence organization-wide decisions that affect AV quality and release confidence.