Machine Learning Engineer 2

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.

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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.

• Design, build, and maintain LLM-based agent workflows, including multi-step reasoning, tool/function calling, and human-in-the-loop approval steps.
• Evaluate and tune LLM usage across multiple providers and model tiers to balance accuracy, latency, and cost for different task types.
• Build and improve retrieval-augmented generation (RAG) pipelines: document processing, chunking, embeddings, and vector search.
• Develop and refine knowledge and memory systems that let the assistant learn from and retrieve domain-specific technical content over time.
• Design prompts, structured tool schemas, and evaluation frameworks to measure and improve agent accuracy and reliability.
• Add observability for LLM and agent behavior — tracing, quality metrics, and regression detection.
• Collaborate with backend engineers to integrate ML components into a production Python service, including data storage and asynchronous processing.
• Run structured experiments on models, prompts, and retrieval strategies, and translate results into shipped improvements.
• Participate in code review, automated testing, and production debugging for ML and agentorchestration code.
• Master's degree in Computer Science, Machine Learning, Electrical Engineering, or a related field.
• 2–4+ years of experience building production ML, NLP, or LLM-powered systems.
• Strong Python skills, including experience with asynchronous programming and typed data modeling.
• Hands-on experience with LLM application development: prompt engineering, tool/function calling, structured output, and output evaluation.