Senior Systems Engineer – CAE AIML Integration & Implementation

General Motors•Austin, TX

7d•Hybrid

About The Position

We are an engineering-focused IT organization responsible for the platforms, tools, and data ecosystems that power GM’s virtual engineering and simulation (CAE) capabilities. Our teams design, build, and operate high-performance computing, data, and AI/ML integration solutions that enable engineers to explore more design options, accelerate development cycles, and improve product quality. We partner closely with CAE, software, data science, and infrastructure teams to turn cutting-edge technologies into robust, production-ready capabilities that make GM’s vehicles safer, more efficient, and more innovative. We are seeking a Senior Systems Engineer to lead the implementation, integration, and operationalization of AI/Machine Learning (AIML) solutions for the CAE engineering community. In this role, you will implement AIML based POC's as well as turn those POC's into robust, supportable capabilities by installing, configuring, and integrating commercial and custom AIML tools with GM’s CAE applications, HPC environments, and data platforms. You will partner closely with CAE engineers, other systems engineers, and infrastructure teams to ensure these solutions are reliable, performant, and straightforward for engineering users to adopt at scale.

Requirements

Bachelor’s degree in Systems Engineering, Software Engineering, Computer Science, Mechanical/Automotive Engineering, or a related technical field.
7+ years of experience in systems engineering and/or software integration roles, with a focus on complex, distributed or HPC-based systems.
Hands-on experience installing, configuring, and integrating engineering or scientific software (e.g., CAE solvers, pre/post tools, optimization/MBSE/AI tools) across desktop, HPC, and/or cloud environments.
Strong background in systems engineering practices (requirements, interfaces, architecture, validation, and lifecycle management).
Practical understanding of AI/ML concepts and patterns (e.g., model endpoints, inference services, pipelines, RAG/agentic workflows) and how they integrate into applications and workflows.
Proficiency with scripting and automation (e.g., Python, shell, CI/CD tools) for installation, configuration, and validation of integrated systems.
Experience working with HPC or cloud-based compute environments (e.g., job schedulers, containers, GPU resources, monitoring and logging tools).
Demonstrated ability to collaborate with cross-functional teams (CAE engineers, software developers, data scientists, infrastructure, cybersecurity) to deliver integrated solutions.
Excellent communication, documentation, and stakeholder-management skills; able to explain complex technical topics to diverse audiences.

Nice To Haves

Master’s degree in Engineering, Systems Engineering, Computer Science, or a related field.
Direct experience with CAE tools and workflows (e.g., structural, CFD, optimization, SPDM/EPDM) and how engineering teams run simulations in practice.
Experience integrating or deploying commercial AIML/physics-AI tools within engineering environments.
Familiarity with GM-like enterprise environments (regulated, safety-critical, or automotive) and associated security and compliance expectations.

Responsibilities

Lead the installation, configuration, and integration of AIML software and services into existing and new CAE workflows, including on-prem and cloud/HPC environments.
Integrate CAE tools, data sources, and AIML components (e.g., APIs, agents, pipelines, UIs) with enterprise platforms such as HPC clusters, Azure, data lakes, and Simulation Process and Data Management solutions.
Define and maintain system-level requirements, interfaces, and architecture diagrams for AIML-enabled CAE solutions, ensuring traceability and alignment with enterprise standards.
Develop and execute installation, integration, and regression test plans to validate end-to-end CAE workflows, including performance, reliability, and security checks.
Partner with CAE engineers and business stakeholders to harden and scale successful AIML POCs, including packaging, deployment, monitoring, and support hand-off.
Establish and improve operational processes (versioning, configuration management, logging, observability, incident response) for AIML-enabled CAE applications.
Ensure all solutions comply with GM security, data governance, and responsible AI guidelines, including appropriate handling of engineering and proprietary data.
Create and maintain user guides, runbooks, and integration documentation to support CAE engineers, support teams, and partner IT organizations.
Provide technical leadership and mentorship to peers and junior engineers on CAE integration patterns, AIML solution deployment, and systems engineering best practices.
Stay current on emerging AIML and CAE software capabilities and recommend pragmatic opportunities to simplify workflows, improve throughput, and reduce cycle time for engineering teams.