AI-Centric Release & Automation Software Engineer

Description
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:
o Release health scores and risk indicators
o 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:
o Use LLMs and AI agents to summarize logs, simulation runs, and telemetry
o Cluster and de-duplicate failures across large validation and fleet runs
o 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:
o Tracked and versioned
o Validated with consistent scenarios and metrics
o Reproducible and explainable to both engineering and safety stakeholders
• Design and govern AI/agent workflows for release operations, including:
o Agent-assisted change impact analysis and regression risk estimation
o Automated ticket creation, routing, and status summarization
o 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:
o Regression tracking
o Flakiness detection and management
o 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:
o Log analysis and anomaly detection
o Failure clustering and root-cause suggestion
o 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: is $123,200 to $189,100. The actual base salary a successful candidate will be offered within this range will vary based on factors relevant to the position. • 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
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