Senior Software Engineer – Autonomy Evaluation

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, turn system-level results into clear feedback for engineering and leadership, and help accelerate validated AV deployment at scale.

WhatYou’llDo (Responsibilities)

Architect and implement metrics and analyses to introspect autonomous driving software performance atsubsysteminterfaces across the autonomy stack; partner closely with autonomy developers and systems engineers.

Design and implement analysis algorithms that summarize, aggregate, and cluster metrics produced by simulations and on-road runs of the autonomy stack.

Propose and develop new statistical and ML methods to quantify performance andidentifypatterns of system and subsystem behavior across diverse scenes and operational domains.

Develop and apply methods to introspect the operation of ML components in the autonomy stack, including evaluation ofperception, prediction, and planning models.

Build andmaintainautonomyevaluationdashboards and interactive reports that provide clear, explainable insights (e.g., trend analysis, drift detection, scenario coverage) for development, verification, and leadership.

Leverage vision-language models (VLMs) and large language models (LLMs), whereappropriate, to classify autonomy performance,identifycritical scenarios, and prioritize validation efforts, integrating human-in-the-loop review where needed.

Maintain a high technical standard through thoughtful system design, code reviews, testing, observability, and adherence to software-engineering best practices.

Interface with cross-organizational partners to articulate requirements, resolve handoff issues, and share best practices around evaluation, metrics, and experiment design.

Your Skills & Abilities(Required Qualifications)

5+ years of applied experience with robotics or autonomous systems software (e.g., sensors,perception, prediction, planning, or control), data analysis, ML evaluation, or autonomy analytics.

3+ years evaluating dynamic systems using numerical and/or ML approaches, including time-series data, state derivatives, dynamics, and interconnected subsystems.

StrongproficiencydevelopingPythonin production team environments, including testing, performance, and code review.

ProficiencywithPandas, NumPy, SciPy,and plotting/visualization libraries for large-scale data analysis and reporting.

Comfort working withC++codebases, including reading, debugging, and instrumenting core algorithms.

A strong curiosity to question anomalous data and systematically root-cause discrepancies.

Demonstrated technical leadership, including driving architectural decisions, influencing cross-team designs, and owning complex features or services end-to-end.

Bachelor’s, Master’s, or PhD inComputer Science, Robotics, Mechanical or Aerospace Engineering, Machine Learning, Data Science, or a related field, or equivalent practical experience.

What Will Give You a Competitive Edge (Preferred Qualifications)

Experience inautonomous driving or field robotics, including visualizing and interpreting results from simulation and field experiments.

Experience evaluating robotics or AV systems usingsensor data(e.g., camera, lidar, radar) and large-scaletime-seriesanalysis.

Strong intuition fordata visualizationand the ability to decompose high-dimensional metrics into clear, trustworthy, and consumable views for technical and non-technical audiences.

Familiarity withstatistical modeling, experimental design, and hypothesistestingfor autonomy or simulation evaluation; fluency with Pandas, NumPy, SciPy, and visualization tools.

ProficiencyinC++ and SQL; experience shaping logging, data schemas, and evaluation pipelines for large-scale autonomy testing and performance monitoring.

Experience working withROS or similar robotics/IPC frameworks, log pipelines, and large-scale experiment databases or evaluation platforms.

Prior development experience withcomputational geometry, linear algebra,PyTorch, and ML techniques applied toperception, prediction, planning, or control.

Background in modelingagent interactionand contributing torelease gatingand safety decisions for autonomy systems.

ExperienceleveragingAI-assisted development and analytics toolsto improve productivity and evaluation coverage.







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