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AI-Centric Release & Automation Software Engineer

  • Ubicación
    • Milford, Michigan
    • Sunnyvale, California
    • Warren, Michigan
  • Tipo de trabajo Full time
  • Publicado
  • Job Requisition JR-202519774

Descripción

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 comfortable 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.

· 5 years of experience and or a combination of educational equivalent - Bachelor’s, Master’s, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field.

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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Adecuaciones (EE.UU. y Canadá)

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