Senior Software Developer– AI Enablement
HybridWarren, Michigan, United States or Milford, Michigan, United States
Warren, Michigan, United States or Milford, Michigan, United StatesHybridFull TimeSenior LevelBachelors DegreeTRANSPORTATIONMedium
Full TimeSenior LevelBachelors DegreeMediumTRANSPORTATION
Job Summary
Develop backend services for virtual ECUs, simulation orchestration, and model execution while building tools for SIL workflows including scenario execution and data capture. Integrate AI/ML components into simulation or validation pipelines and design APIs for simulation control, artifact management, and orchestration. Optimize performance for compute-intensive workloads and contribute to CI/CD workflows for simulation and AI components. Collaborate with DevOps and simulation teams to ensure seamless integration and improve package consistency, test coverage, and observability.
Required Qualifications
- 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
- This role is categorized as hybrid
- The selected candidate is expected to report to a specific location at least 3 times a week {or other frequency dictated by their manager}
- The selected candidate will be required to travel <25% for this role
Desired Qualifications
- 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)
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