R&D AI Software Development Engineer
Summary
Entry-level AI software engineer on Keysight's R&D team building AI-assisted workflows for EDA and simulation tools: writing Python components, APIs and tests, integrating LLM agents via MCP-based tool integrations, and prototyping AI-agent features like simulation setup help and report generation. Core tech: Python, LLMs, embeddings/RAG, Git.
Qualifications Technical Qualifications Bachelor's or master's degree in computer science, electrical engineering, software engineering, artificial intelligence, applied mathematics, or a related technical field. Good programming skills in Python, with an interest in writing clean, maintainable, tested software. Basic understanding of AI/ML concepts such as neural networks, embeddings, model evaluation, prompt engineering, retrieval-augmented generation, or structured outputs. Interest in AI-agent or copilot-style applications using LLMs, tool calling, workflow automation, and integration with external tools or APIs. Familiarity with software-development practices such as Git, unit testing, code review, Agile teamwork, issue tracking, and technical documentation.
Preferred Qualifications Internship, academic project, thesis, or early professional experience involving AI/ML, LLM applications, automation, engineering software, or scientific computing. Exposure to EDA, CAD, simulation, high-speed digital design, signal integrity, power integrity, RF/microwave design, photonics, or other engineering domains is a plus. Familiarity with neural-network-based surrogate modeling, design-space exploration, optimization, or simulation acceleration is beneficial but not required. Exposure to vector databases, semantic search, knowledge graphs, agent frameworks, or cloud-based development environments is an advantage. Experience with C++, C#, TypeScript, JavaScript, or desktop/web application integration is a plus.
Soft Skills & Collaboration Curious, eager to learn, and comfortable working in a fast-evolving AI and engineering software environment. Able to communicate clearly with senior engineers, product stakeholders, and domain experts. Strong problem-solving mindset, attention to detail, and willingness to iterate based on feedback. Collaborative team player who can contribute to multi-site and multi-disciplinary R&D projects.
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