Machine Learning (ML) Engineer

Keysight Technologies; Barcelona; Presencial; EUR 85.000 - 125.000; Jornada completa

Descripción de la vacante

Keysight Technologies seeks an experienced ML Engineer to design and implement knowledge layers for SOS AI, an AI platform bridging EDA and AI/ML workflows. You will build low-latency retrieval, RAG, and graph-based systems for IP-sensitive data in on-prem deployments.

You will advance embeddings and LLM-based components, ensure governance and provenance, and collaborate with product and EDA teams to translate semiconductor workflows into technical requirements.

Formación

• MS or PhD in Computer Science, Electrical Engineering, or related field.
• 5+ years building production ML or data-intensive systems.
• Must have production experience with RAG and knowledge graphs (ingestion, indexing, retrieval).
• Hands-on experience with LLMs, embeddings, prompt and context engineering, evaluation, and on-premise inference.
• Strong experience with vector and graph databases and low-latency retrieval infrastructure.

Responsabilidades

• Design and build low-latency hybrid retrieval over large, heterogeneous data for on-premises 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 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.

Conocimientos

RAG systems

Knowledge graphs

LLMs and embeddings

Low-latency retrieval

Python

Production ML

Educación

MS or PhD in Computer Science or Electrical Engineering

Herramientas

PyTorch

Hugging Face

Vector databases

Graph databases

MCP (Model Context Protocol)

Descripción del empleo

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.

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.***