Senior Software Developer– AI Enablement

General Motors•Milford, MI

2d•Hybrid

About The Position

The CoSim Infrastructure, Quality, and Scalability team, within GM’s Virtualization and SIL Integration organization, builds the foundational software, automation, and integration capabilities that make virtual ECUs and co-simulation environments reliable, reusable, and scalable across vehicle domains. The team develops and evolves CoSim/SIL infrastructure—including SoftECU and FMI-based capabilities, RestBus and SIMETH connectivity, HWIO and calibration enablement, build and CI/CD pipelines, and automated quality gates—while improving package consistency, test coverage, observability, and defect triage. Their work enables engineering, calibration, and verification teams to validate embedded-platform software earlier and with greater confidence, supporting daily regression testing as well as complex, multi-variant, multi-domain virtual validation. Within the broader Virtualization and SIL Integration organization, the team collaborates closely with domain teams to deliver high-fidelity virtual environments and advance GM’s software-defined vehicle development process. As a Senior Software Developer, you will build the software that powers GM’s virtual development environments, SIL workflows, and AI‑enhanced simulation capabilities. You bring strengths in automation, embedded systems, and problem‑solving — and this role expands your exposure to cloud, data, ML fundamentals, and large‑scale simulation architecture.

Requirements

Bachelor’s degree (or higher) in Engineering, Computer Science, or related field.
7+ years of relevant experience in software development, simulation, or embedded systems.
Strong programming skills in Python, C++, C#, or Java.
Experience with simulation or virtualization (vECUs, FMUs, SIL).
Understanding of cloud services and distributed systems.
Experience with CLI-based architecture for tools design.
Knowledge of MCP-based architecture for AI tools design.
Experience with databases for simulation metadata and results.

Nice To Haves

Optional AI skills: ML lifecycle basics, Vector search or embeddings, and model integration.
Experience with microservices for simulation orchestration.
Knowledge of Kubernetes for running compute workloads.
Performance tuning expertise for simulation or AI pipelines.
Experience with automotive data and domain modeling.
Experience with ontology-based engineering processes and architecture frameworks.
Advanced knowledge of ontology technologies and ontology design patterns (Owl, RDF, SPARQL, Automated reasoning).

Responsibilities

Develop backend services supporting virtual ECUs, simulation orchestration, and model execution.
Build tools for SIL workflows including scenario execution, data capture, and automation.
Integrate AI/ML components into simulation or validation pipelines.
Design APIs for simulation control, artifact management, and orchestration.
Optimize performance for compute‑intensive workloads.
Collaborate with DevOps and simulation teams to ensure seamless integration.
Contribute to CI/CD workflows for simulation and AI components.