Machine Learning (ML) Engineer

Overview:

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.

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.
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.

• 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.

Este anuncio proviene de Indeed. Ver anuncio original ↗