AI-Centric Release & Automation Software Engineer

The Role You will be part of a core team that ensures safe, reliable, and scalable releases of the Autonomous Vehicle (AV) software stack through automation, AI-driven analysis, and systematic validation processes. The mission is to accelerate the velocity, safety, and stability of ML-driven AV releases by unifying software engineering, reliability analysis, AI/ML, and release automation under one cohesive framework. In this position, you will collaborate closely with Release Engineers, Systems Engineers, DevOps, and AI/ML teams to design and implement automated release validation pipelines, build metrics for release health and stability, and drive root-cause investigations of software and system issues impacting production readiness. You will also help define and deploy LLM- and agent-based workflows that augment release and triage operations. If you are passionate about improving the safety, reliability, and velocity of ML-driven AV software releases through intelligent automation, AI/LLM-powered tooling, and systems thinking, we want to talk to you. What You’ll Be Doing Design and implement automated release pipelines that integrate simulation, on‑road, and CI/CD validation signals to assess software readiness for deployment. Establish release reliability metrics and dashboards to quantify build quality, regression trends, and confidence in software deployment, including: Release health scores and risk indicators Stability and flakiness trends over time Collaborate with AI/ML, Simulation, and Systems Engineering teams to ensure robust, reproducible, and scalable evaluation of release candidates, including ML model rollouts and AV behavior changes. Develop automated triage and failure analysis systems that: Use LLMs and AI agents to summarize logs, simulation runs, and telemetry Cluster and de-duplicate failures across large validation and fleet runs Propose likely root causes, impacted components, and suggested owners Integrate and evolve data pipelines for continuous monitoring of release health, including automated collection and transformation of test, simulation, and telemetry data into features consumable by AI/ML systems and reliability tooling. Drive systematic improvements in release readiness criteria, defining measurable gates and AI‑informed pass/fail logic tied to product safety, reliability standards, and model governance policies. Develop frameworks for continuous release validation, ensuring each ML or software iteration is: Tracked and versioned Validated with consistent scenarios and metrics Reproducible and explainable to both engineering and safety stakeholders Design and govern AI/agent workflows for release operations, including: Agent‑assisted change impact analysis and regression risk estimation Automated ticket creation, routing, and status summarization Human‑in‑the‑loop review loops to refine and improve agent behavior over time Communicate insights and reliability findings—including AI‑generated summaries and recommendations—to developers, QA, and leadership to influence roadmap prioritization, technical debt mitigation, and release decisions. Map reliability, automation, and AI‑assisted processes to the broader safety case and release governance framework, ensuring compliance with relevant standards and internal policies. What You Must Have Strong proficiency in Python (and comfort with SQL) for building AI‑powered agents, LLM tooling, and data workflows that support analysis, triage, and automation. Hands‑on experience building or integrating AI/ML or LLM‑powered systems into real workflows (e.g., triage bots, summarization tools, retrieval‑augmented assistants, anomaly detection, or decision‑support systems). Experience designing, orchestrating, or operating multi‑step AI/agent workflows, including tool calling, chaining, routing, and human‑in‑the‑loop feedback loops. Familiarity with the modern LLM tooling ecosystem, such as vector stores/embeddings, prompt and tool design, evaluation frameworks, and guardrails/safety controls. Solid understanding of system reliability concepts , including: Regression tracking Flakiness detection and management Automated triage and incident routing Strong analytical, debugging, and problem‑solving skills across large‑scale or real‑time software systems. Track record of cross‑functional collaboration across engineering, QA, operations, and AI/ML teams. Ability to learn quickly and operate effectively in a dynamic, high‑stakes, safety‑critical environment. Excellent communication skills for presenting AI‑ and data‑driven insights to engineering and leadership stakeholders, including clear written summaries, prompts/specs, and decision‑ready narratives. Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field—or equivalent experience. Bonus Points Experience with release governance frameworks for ML or AV systems (e.g., model rollout policies, shadow/ A‑B testing, guardrails, or kill switches). Proven experience with CI/CD systems (e.g., GitHub Actions, Jenkins, GitLab, or equivalent) used to run automated tests and validation workflows. Knowledge of AV/ADAS software architectures and simulation‑to‑road validation loops, including perception, planning, and control components. Experience building metrics and analytics pipelines in cloud environments (AWS, GCP, or Azure) to support large‑scale validation and monitoring. Familiarity with data visualization and observability tools (Grafana, Superset, Power BI, etc.) for release health and reliability dashboards. Experience using Jira, GitHub Projects, or equivalent tools for release tracking and reliability triage, especially where AI, rules, or automation are used to assist prioritization and workflow routing. Hands‑on experience applying AI/ML or LLMs to: Log analysis and anomaly detection Failure clustering and root‑cause suggestion Automated summarization of complex test outcomes, release status, and risk profiles Compensation The compensation information is a good faith estimate only. It is based on what a successful applicant might be paid in accordance with applicable state laws. The compensation may not be representative for positions located outside of New York, Colorado, California, or Washington. The salary range for this role: $123,200 to $189,100. Bonus Potential: An incentive pay program offers payouts based on company performance, job level, and individual performance. Benefits: GM offers a variety of health and wellbeing benefit programs. Benefit options include medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts and more. #J-18808-Ljbffr General Motors