Job Requirements
Grand Rapids, MI
Clearance Unspecified Polygraph not specified
Career Level not specified
Salary not specified
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Job Description
Design, build, and deploy LLM-powered tools that support software development activities such as code generation, code review, debugging, refactoring, automated test creation, and documentation.
Develop retrieval-augmented generation (RAG) pipelines that ground model outputs in internal engineering artifacts including codebases, requirements, design documentation, standards, and domain knowledge.
Create agentic workflows that interact with engineering toolchains such as compilers, static analyzers, test frameworks, version control systems, and issue-tracking platforms.
Evaluate and select models based on performance, cost, latency, licensing, and security requirements.
Apply techniques such as prompt engineering, fine-tuning, and parameter-efficient adaptation to improve model usefulness for aerospace and embedded software use cases.
Optimize model deployment for secure, on-premises, air-gapped, and classified environments.
Integrate AI capabilities into developer workflows, including IDE experiences, CI/CD pipelines, merge-request checks, and software quality processes.
Establish evaluation methods, benchmarks, and guardrails to measure and improve accuracy, security, reliability, and usability.
Help ensure AI-assisted outputs support software assurance, traceability, and human accountability in certified software environments.
Partner closely with software, systems, test, cybersecurity, and business teams to identify high-value use cases and transition solutions into broader engineering adoption.
Mentor others, share best practices, and help define how GE Aerospace scales the use of generative AI in engineering.
Required Qualifications
Bachelor’s degree in Computer Science, Software Engineering, or related STEM field.
5+ years of experience in software engineering, machine learning engineering, or a closely related technical field.
Hands-on experience developing software solutions in production or operational environments.
This role requires the successful candidate to maintain a US Government Security Clearance; prerequisite for a security clearance is U.S. Citizenship.
Active Security Clearance.
Willingness to work onsite in Grand Rapids, MI - Corporate relocation assistance provided.
Preferred Qualifications:
Master’s degree in Computer Science, Machine Learning, or related field.
Experience building and deploying LLM-based applications such as RAG systems, AI coding assistants, copilots, or agent-based workflows.
Strong programming skills in Python, with experience using modern ML/LLM frameworks such as PyTorch, Hugging Face Transformers, LangChain, or LlamaIndex.
Working knowledge of C/C++ and embedded software development environments.
Experience with vector databases, embeddings, and search technologies.
Familiarity with fine-tuning, quantization, and efficient inference serving for production-scale LLM systems.
Experience integrating AI capabilities into DevSecOps and software engineering workflows.
Background in aerospace, defense, medical, automotive, or other mission-critical industries.
Familiarity with software assurance or certification considerations such as DO-178C, MISRA, or CERT C/C++.
Knowledge of AI security, data governance, and responsible AI practices.
Experience deploying solutions in secure, disconnected, or classified environments.
Strong problem-solving, communication, and collaboration skills.
Develop retrieval-augmented generation (RAG) pipelines that ground model outputs in internal engineering artifacts including codebases, requirements, design documentation, standards, and domain knowledge.
Create agentic workflows that interact with engineering toolchains such as compilers, static analyzers, test frameworks, version control systems, and issue-tracking platforms.
Evaluate and select models based on performance, cost, latency, licensing, and security requirements.
Apply techniques such as prompt engineering, fine-tuning, and parameter-efficient adaptation to improve model usefulness for aerospace and embedded software use cases.
Optimize model deployment for secure, on-premises, air-gapped, and classified environments.
Integrate AI capabilities into developer workflows, including IDE experiences, CI/CD pipelines, merge-request checks, and software quality processes.
Establish evaluation methods, benchmarks, and guardrails to measure and improve accuracy, security, reliability, and usability.
Help ensure AI-assisted outputs support software assurance, traceability, and human accountability in certified software environments.
Partner closely with software, systems, test, cybersecurity, and business teams to identify high-value use cases and transition solutions into broader engineering adoption.
Mentor others, share best practices, and help define how GE Aerospace scales the use of generative AI in engineering.
Required Qualifications
Bachelor’s degree in Computer Science, Software Engineering, or related STEM field.
5+ years of experience in software engineering, machine learning engineering, or a closely related technical field.
Hands-on experience developing software solutions in production or operational environments.
This role requires the successful candidate to maintain a US Government Security Clearance; prerequisite for a security clearance is U.S. Citizenship.
Active Security Clearance.
Willingness to work onsite in Grand Rapids, MI - Corporate relocation assistance provided.
Preferred Qualifications:
Master’s degree in Computer Science, Machine Learning, or related field.
Experience building and deploying LLM-based applications such as RAG systems, AI coding assistants, copilots, or agent-based workflows.
Strong programming skills in Python, with experience using modern ML/LLM frameworks such as PyTorch, Hugging Face Transformers, LangChain, or LlamaIndex.
Working knowledge of C/C++ and embedded software development environments.
Experience with vector databases, embeddings, and search technologies.
Familiarity with fine-tuning, quantization, and efficient inference serving for production-scale LLM systems.
Experience integrating AI capabilities into DevSecOps and software engineering workflows.
Background in aerospace, defense, medical, automotive, or other mission-critical industries.
Familiarity with software assurance or certification considerations such as DO-178C, MISRA, or CERT C/C++.
Knowledge of AI security, data governance, and responsible AI practices.
Experience deploying solutions in secure, disconnected, or classified environments.
Strong problem-solving, communication, and collaboration skills.
group id: 10293868
It's a great time to work at GE Aerospace!
