Senior AI-Centric Release & Automation Software Engineer

The Role

You will be part of a core team that enables safe reliable and scalable releases of the Autonomous Vehicle (AV) softwarestackthrough 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 engineeringprocesses.

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 helpestablishpractical standards for evaluating governing and scaling automation and AI solutions while improving release readiness software quality and engineeringproductivity.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.

WhatYoullBe Doing

• Lead the design and implementation of automation across software development testing release and operational workflows.

• Identifyopportunities to apply AI machine learning and LLM-based tools to improveengineeringproductivity and decision-making.

• Build AI-enabled solutions for test analysis failure classification defect triage documentation reporting and workflow orchestration.

• Develop andmaintainscalableCI/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 manualprocesses and measure improvements in cycle time test efficiency defect prevention and engineering throughput.

• Improvevisibility into test health regression trends flaky tests failure patterns and release readiness.

• Collaborate with engineering QA operations data andprogram 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 findingsprocess improvements and measurable business impact to engineering and leadership stakeholders.

What You Must Have

• StrongproficiencyinPythonandSQL.

• Provenexperience inCI/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 improveengineeringproductivity 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 andproblem-solving skills across large-scale software systems.

• Experience integratingsimulation or hardware-in-loop testinginto automated pipelines.

• Track recordofcross-functional collaborationacross engineering QA and operations teams.

• Ability tolearn quicklyandoperateeffectively in a dynamic high-stakes environment.

• Excellent communication skills for presenting data-driven insights to engineering and leadership stakeholders.

• Bachelors Masters or PhD in Computer Science Electrical Engineering Robotics ora relatedfieldor equivalent experience.

Bonus Points!

• Experience developing AI agents copilots retrieval-augmented generation systems workflow automation or intelligent engineering tools.

• Experienceestablishinggovernance evaluation monitoring and security practices for AI-enabled engineering solutions.

• Knowledge of AV/ADAS software architectures simulation validation loops or automated vehicle testing.

• Experience withreleasegovernance quality gates or complianceprocesses 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 GitHubProjects 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.

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 $153200 to $234100. 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