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Job Description
The AV Runtime team within GM’s Autonomous Vehicle Platform Core organization builds software and data systems that turn real-world driving into high-quality training and evaluation data for autonomous driving.
Our systems collect sensor, vehicle, and scenario data used by teams developing perception, prediction, planning, and other autonomous-driving capabilities. We focus on capturing the right data from the fleet, preserving privacy, maintaining data quality, and reliably moving vehicle data into the AI development pipeline.
We build the software that helps autonomous vehicles learn from the real world.
You will own projects and technical workstreams that improve the reliability, observability, and operability of fleet-scale vehicle data recording and delivery. You will build Python software across cloud, backend, and data platforms so high-value vehicle and sensor data is captured, transferred, processed, and made usable for autonomous vehicle development.
You will refine requirements, define architecture and execution plans, lead design reviews and reliability investigations, resolve non-routine integration issues, and deliver production-ready solutions that improve service health, data quality, reliability, performance, privacy, and operational readiness. You will connect vehicle symptoms to cloud behavior, identify systemic failure patterns, drive cross-team corrective actions, and apply AI and LLMs to triage, telemetry analysis, issue classification, root-cause investigation, and engineering automation.
You will work across Python services, telemetry pipelines, cloud infrastructure, backend services, data storage, monitoring, metrics, and reliability workflows. You will collaborate across partner organizations—including cloud platform, backend, systems engineering, fleet operations, data engineering, and downstream consumers—to deliver reliable, diagnosable, and scalable fleet data capabilities.
Own project-level initiatives that improve the reliability of vehicle-to-cloud data recording and telemetry workflows.
Develop Python software for telemetry processing, data validation, metrics, reliability automation, and operational tooling.
Build components that capture, filter, validate, package, store, and deliver vehicle and sensor data.
Define data-quality checks, metric semantics, dashboards, service-level indicators, and operational health measures.
Debug complex issues across data pipelines, cloud ingestion, storage, distributed processing, monitoring systems, and backend integrations.
Apply AI and large language models (LLMs) to accelerate issue discovery, failure classification, root-cause analysis, knowledge retrieval, and workflow automation.
Collaborate across cloud, backend, data engineering, fleet operations, and downstream teams to align interfaces, metrics, ownership, and operating expectations.
Improve performance, scalability, observability, privacy, availability, and cloud cost across data workflows.
Deliver reliable software through clear requirements, design reviews, code reviews, automated testing, CI, staged rollout, monitoring, and incident response.
Mentor engineers and contribute to reusable patterns, documentation, tooling, and continuous engineering improvement.
Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, Robotics, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
Experience delivering production-quality Python software, data workflows, or reliability projects with limited guidance.
Experience with embedded SW, Robotics, Automotive, or a similar tech stack.
Strong fundamentals in data structures, algorithms, operating systems, distributed systems, networking, and software design.
Strong Python coding ability, including writing maintainable, testable, observable, and performance-conscious software.
Experience with telemetry processing, data pipelines, cloud services, backend integrations, monitoring, or reliability automation.
Ability to debug complex software, cloud, distributed-system, and data-integration issues and drive root-cause resolution.
Experience owning technical requirements, plans, milestones, risks, and delivery for a project, feature area, or workstream.
Ability to communicate technical tradeoffs clearly and collaborate effectively across engineering teams and organizations.
Experience building Python services, cloud applications, data-processing workflows, or reliability automation.
Experience designing or operating cloud distributed architectures using Kubernetes, Google Cloud services, BigQuery, cloud storage, backend services, or distributed data-processing systems.
Exposure to C++ systems programming, IPC, messaging, networking, serialization, or data pipelines.
Experience with cloud platforms, Kubernetes, container orchestration, cloud storage, backend services, or distributed data processing.
Experience with telemetry, logging, observability, data quality, metrics, dashboards, or fleet-data recording systems.
Experience with APIs, messaging, networking, serialization, storage, SQL, data contracts, or event-driven workflows.
Experience applying AI or LLMs, data analysis, incident triage, workflow automation, or engineering productivity.
Experience with fleet deployment, release monitoring, incident response, root-cause analysis, service-level indicators, or operational readiness.
Experience mentoring engineers, leading design reviews, or driving cross-team and cross-organization technical initiatives.
Prior experience in autonomous driving, distributed systems, data infrastructure, AI/ML infrastructure, platform software, or production operations.
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 128,000 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:
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.
If selected out of Washington, in the future you will need to go to office in Seattle, WA.
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