AI-Centric Release & Automation Software Engineer

General Motors•Sunnyvale, CA

1d•$153,200 - $234,100

About The Position

You will be part of a core team that enables safe, reliable, and scalable releases of the Autonomous Vehicle (AV) software stack through intelligent automation, AI-enabled engineering workflows, and data-driven validation. The mission is to accelerate AV software development and release velocity by reducing manual effort, improving test and release visibility, and applying AI to engineering processes. In this position, you will collaborate closely with Release Engineers, Systems Engineers, DevOps, QA, and AI/ML teams to design and implement automated release validation pipelines, integrate simulation and hardware-in-loop testing, build engineering metrics, and develop AI-enabled solutions for test analysis, failure classification, defect triage, reporting, and workflow orchestration. You will help establish practical standards for evaluating, governing, and scaling automation and AI solutions while improving release readiness, software quality, and engineering productivity. If you are passionate about applying intelligent automation and systems thinking to accelerate the development of safe, high-quality ML-driven AV software, we want to talk to you.

Requirements

Strong proficiency in Python and SQL.
Proven experience in CI/CD systems (e.g., GitHub Actions, Jenkins, GitLab, or equivalent).
Hands-on experience developing ELT/ETL pipelines and integrating data from engineering, QA, simulation, and operational systems.
Experience applying AI, machine learning, or LLM-based solutions to improve engineering productivity, test analysis, defect triage, documentation, or decision-making.
Ability to evaluate AI-generated outputs for accuracy, consistency, traceability, and usefulness in engineering workflows.
Strong analytical, debugging, and problem-solving skills across large-scale software systems.
Experience integrating simulation or hardware-in-loop testing into automated pipelines.
Track record of cross-functional collaboration across engineering, QA, and operations teams.
Ability to learn quickly and operate effectively in a dynamic, high-stakes environment.
Excellent communication skills for presenting data-driven insights to engineering and leadership stakeholders.
Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field—or equivalent experience.

Nice To Haves

Experience developing AI agents, copilots, retrieval-augmented generation systems, workflow automation, or intelligent engineering tools.
Experience establishing governance, evaluation, monitoring, and security practices for AI-enabled engineering solutions.
Knowledge of AV/ADAS software architectures, simulation validation loops, or automated vehicle testing.
Experience with release governance, quality gates, or compliance processes for ML, AV, or safety-critical systems.
Familiarity with reliability engineering concepts such as MTBF, FMEA, reliability growth analysis, and failure trend analysis.
Experience building automation and metrics pipelines in AWS, GCP, Azure, or equivalent cloud environments.
Familiarity with data visualization and observability tools such as Grafana, Superset, Power BI, or equivalent.
Experience integrating Jira, GitHub Projects, or similar tools into automated release tracking, workflow orchestration, or engineering triage.
Experience measuring automation impact through cycle-time reduction, defect prevention, reduced manual effort, improved test efficiency, or increased engineering throughput.

Responsibilities

Lead the design and implementation of automation across software development, testing, release, and operational workflows.
Identify opportunities to apply AI, machine learning, and LLM-based tools to improve engineering productivity and decision-making.
Build AI-enabled solutions for test analysis, failure classification, defect triage, documentation, reporting, and workflow orchestration.
Develop and maintain scalable CI/CD integrations supporting simulation, hardware-in-loop, regression, and release validation activities.
Build data pipelines that combine engineering, QA, simulation, test, and release information into actionable insights.
Establish practical methods for evaluating the accuracy, usefulness, traceability, and adoption of AI-enabled engineering tools.
Automate repetitive manual processes and measure improvements in cycle time, test efficiency, defect prevention, and engineering throughput.
Improve visibility into test health, regression trends, flaky tests, failure patterns, and release readiness.
Collaborate with engineering, QA, operations, data, and program teams to understand pain points and deliver effective automation solutions.
Integrate tools such as Jira, GitHub, dashboards, observability platforms, and cloud services into unified engineering workflows.
Help define standards and governance for maintainable, secure, observable, and scalable automation and AI solutions.
Communicate technical findings, process improvements, and measurable business impact to engineering and leadership stakeholders.