Remote Machine Learning Developer

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.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.Design and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments.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.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.Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.Familiarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.

Benchmark retrieval quality, embedding models, and LLMs against EDA use cases, selecting models per task under cost and latency constraints. 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. Proficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices. Familiarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.

Barcelona, España

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. 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. Design and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments. 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.