Machine Learning (ML) Engineer

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. Learn more about what we do.

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.

We are looking for a Machine Learning (ML) Engineer to join our industry-leading data and IP management product team to build the knowledge and intelligence layers of SOS AI, our AI platform serving the intersection between Electronic Design Automation (EDA) and AI/ML workflows.

Responsibilities

• Design and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments.
• Develop the semantic insight layer: automated tagging, domain-aware metadata, and embeddings as first-class managed assets with version provenance.
• Build the EDA-aware knowledge graph as organizational memory: entity and relationship inference, ontology evolution, versioning, and temporal queries.
• Implement agentic memory and outbound MCP servers exposing retrieval, graph traversal, and lineage to external agents with access controls gatekeeping and full audit.
• Engineer governance so access control propagates from source data through embeddings, graph nodes, retrievals, and agent responses.
• Benchmark retrieval quality, embedding models, and LLMs against EDA use cases, selecting models per task under cost and latency constraints.
• Collaborate with product, EDA tool teams and customers to translate semiconductor and RF workflows into requirements.

Qualifications

• MS or PhD in Computer Science, Electrical Engineering, or related field
• 5+ years building production ML or data-intensive systems.
• Demonstrated experience building RAG and knowledge graph systems in production (a must): ingestion, indexing, and retrieval pipelines.
• Hands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.
• Strong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.
• Experience with agentic memory management and the Model Context Protocol (MCP) or comparable agent-grounding interfaces.
• Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.
• ML applied to semiconductor applications, especially the RF and microwave industry, is highly valued.
• Familiarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.

Careers Privacy Statement ***Keysight is an Equal Opportunity Employer.***

• Design and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments.
• Develop the semantic insight layer: automated tagging, domain-aware metadata, and embeddings as first-class managed assets with version provenance.
• Build the EDA-aware knowledge graph as organizational memory: entity and relationship inference, ontology evolution, versioning, and temporal queries.
• Implement agentic memory and outbound MCP servers exposing retrieval, graph traversal, and lineage to external agents with access controls gatekeeping and full audit.
• Engineer governance so access control propagates from source data through embeddings, graph nodes, retrievals, and agent responses.
• Benchmark retrieval quality, embedding models, and LLMs against EDA use cases, selecting models per task under cost and latency constraints.
• Collaborate with product, EDA tool teams and customers to translate semiconductor and RF workflows into requirements.

• MS or PhD in Computer Science, Electrical Engineering, or related field
• 5+ years building production ML or data-intensive systems.
• Demonstrated experience building RAG and knowledge graph systems in production (a must): ingestion, indexing, and retrieval pipelines.
• Hands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.
• Strong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.
• Experience with agentic memory management and the Model Context Protocol (MCP) or comparable agent-grounding interfaces.
• Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.
• ML applied to semiconductor applications, especially the RF and microwave industry, is highly valued.
• Familiarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.

Careers Privacy Statement ***Keysight is an Equal Opportunity Employer.***