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AI-Centric Release & Automation Software Engineer

General Motors

Sunnyvale, CA, US$153k – $234khybrid

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About this role

Job Description The Role    

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 velo ci ty by redu ci ng manual effort, im pro v ing test and release visibility, and applying AI to engineering   pro cesses.  

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 im pro v ing release readiness, software quality, and engineering   pro ductivity.   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.  

What  You’ll  Be Doing    

• 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 im pro v e   engineering   pro ductivity and de ci sion-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   pro cesses and measure im pro v e ments in cycle time, test effi ci ency, defect prevention, and engineering throughput.  

• Im pro v e   visibility into test health, regression trends, flaky tests, failure patterns, and release readiness.  

• Collaborate with engineering, QA, operations, data, and   pro gram 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,   pro cess im pro v e ments, and measurable business impact to engineering and leadership stakeholders.  

What You Must Have    

• Strong  pro fi ci ency  in  Python  and  SQL .   

• Pro v e n   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 im pro v e   engineering   pro ductivity, test analysis, defect triage, documentation, or de ci sion-making.  

• Ability to evaluate AI-generated outputs for accuracy, consistency, traceability, and usefulness in engineering workflows.  

• Strong analytical, debugging, and   pro blem-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 S ci ence, Electrical Engineering, Robotics, or  a related  field—or equivalent experience.   

Bonus Points!    

• 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   pro cesses 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   Pro jects, 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, im pro v e d test effi ci ency, 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 $153,200 to $234,100. 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 per

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