Senior Autonomy Behavior Validation Engineer

GM•Sunnyvale, CA

About The Position

As a Senior Autonomy Behavior Validation Engineer on the Software Validation team within the AV organization, you will play a critical role in simulation-led validation of autonomous vehicle behavior, with a strong focus on behavioral analysis of ML models, automation, and agentic workflows. You will leverage your experience in software engineering to convert validation strategies into well-architected, automated pipelines and tools that analyze AV behavior at scale. You will work with a team of engineers to define best practices, raise the bar internally on coding quality and automation, and evaluate the safety and performance of autonomous systems. You will be responsible for shaping the future of evaluation methodologies for ML systems, architecting solutions that meet the testing needs of AI developers, systems engineers, and safety stakeholders. The Autonomous Vehicle (AV) organization is dedicated to advancing the development of autonomous vehicles through cutting-edge simulation technologies and novel iterative development processes. The Software Validation team focuses on unlocking software launches and continuous release decisions via simulation-led verification and validation strategies, prototypes, and protocols. Our collaborative environment fosters innovation and excellence, allowing us to push the boundaries of what is possible in autonomous vehicle testing.

Requirements

5+ years of experience and MS/PhD in Computer Science, Machine Learning, Robotics, Software Engineering, Data Science, or a related field.
Background in autonomous vehicles
Strong programming skills in Python and experience with SQL; exposure to C++ or another compiled language is preferred.
Experience writing clean, well-tested, and maintainable code for data processing, backend services, or scientific/analytical workflows.
Hands-on experience using AI agents, LLM-based tools, or workflow orchestration to automate parts of the development, validation, or operations lifecycle.
Experience working with large datasets to derive insights, build analyses, or drive decisions.
Strong analytical thinking skills with the ability to interpret data and derive impactful conclusions.
Ability to adapt and operate under ambiguity, going from quick code prototypes to longer-term, production-ready solutions on brief time horizons.
Excellent communication skills, capable of switching between high-level and detailed technical discussions.
Curiosity and willingness to learn new tools and domains, especially in autonomous systems, robotics, and safety-critical software.

Nice To Haves

3-5 years of professional engineering experience building automation, internal tools, or data/analysis pipelines.
Proven track record of successful software engineering on a safety-critical or high-reliability product, especially as it relates to verification and validation tools, testing frameworks, or CI/CD automation.
Reusable internal libraries or frameworks that are used by other engineers.
Demonstrated impact from introducing automation and AI-assisted tooling (e.g., AI agents, AI validation critics, smart monitoring) that improved scale, reliability, or engineering velocity in ML validation workflows.
Experience working with robotics simulation environments or simulation-based testing.
Experience developing dashboards and data visualizations using tools such as Looker, Jupyter notebooks, or similar platforms to communicate validation results to stakeholders.

Responsibilities

Build validation critics and agentic workflows to evaluate autonomy behavior, automate scenario review, and accelerate validation flows.
Drive simulation-based ML evaluation at scale by evaluating deep learning modules in realistic sensor and traffic simulation and expanding behavioral and scenario coverage tightly linked to ML models.
Translate validation strategies into production-quality code and automation pipelines that execute high-quality AV behavior analysis for continuous and scaled software release cycles.
Ensure the quality and reliability of behavior validation outputs through monitoring, alerting, automated checks, and continuous improvement of the underlying code and data pipelines.
Provide cross-functional collaboration across Simulation, Safety, Systems Engineering, and Autonomy focused on high-quality, automation-first software.