R&D AI Software Development Engineer

Overview

Keysight is at the forefront of technology innovation, delivering breakthroughs and trusted insights in electronic design, simulation, prototyping, test, manufacturing, and optimization. Our ~16,800 employees create world-class solutions in communications, 5G, automotive, energy, quantum, aerospace, defense, and semiconductor markets for customers in over 100 countries.

Our award-winning culture embraces a bold vision of where technology can take us and a passion for tackling challenging problems with industry-first solutions. We believe that when people feel a sense of belonging, they can be more creative, innovative, and thrive at all points in their careers.

About the Team

You will join a Keysight R&D team developing AI-enabled workflow capabilities for Electronic Design Automation (EDA), High-Speed Digital (HSD), signal integrity, power integrity, and related simulation-driven engineering domains. The team combines product APIs, Python automation, AI/ML techniques, neural-network concepts, and agentic workflows to make advanced design tools easier to use and more productive for engineers.

Responsibilities

As an AI Software Development Engineer, you will contribute to the development of AI-assisted engineering workflows that connect large language models, EDA applications, simulation tools, internal knowledge sources, and product APIs. You will work with senior engineers to build reliable software components, prototype AI-agent capabilities, and support practical automation use cases for design and simulation workflows.

• Develop Python-based software components, scripts, tests, and APIs that support AI-assisted EDA workflows.
• Contribute to MCP-based tool integration, including tool definitions, prompts, resources, and safe invocation patterns under guidance from senior engineers.
• Prototype AI-agent capabilities such as workflow guidance, simulation setup assistance, result summarization, report generation, and design-analysis support.
• Apply basic AI/ML and neural-network concepts, including embeddings, retrieval, model evaluation, surrogate-model awareness, and data preparation for engineering use cases.
• Participate in code reviews, testing, documentation, debugging, and continuous improvement of production software quality.

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.