{"id":2256359,"url":"https://alion.io/job/keysight-machine-learning-ml-engineer","title":"Machine Learning (ML) Engineer","company":{"id":53596,"name":"Keysight","domain":"keysight.com","url":"https://alion.io/company/keysight","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Jibe","truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Barcelona, Spain"],"countries":["ES"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":60000,"max_usd":128000,"period":"year","method":"role_seniority_country_cell","sample_n":8},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"Context Engineering","optional":false},{"name":"Embeddings","optional":false},{"name":"Fine-tuning","optional":false},{"name":"Hugging Face","optional":false},{"name":"Knowledge Graph","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"RAG","optional":false}],"status":"live","first_seen_at":"2026-09-09T07:43:00Z","employer_posted_date":"2026-10-11","last_verified_at":"2026-10-11T17:40:26Z","board_verified":true,"closed_at":null,"days_open":32,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":32},"description":"OverviewKeysight 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.\nOur 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.\nWe 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.\nResponsibilities\nDesign and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments.\nDevelop the semantic insight layer: automated tagging, domain-aware metadata, and embeddings as first-class managed assets with version provenance.\nBuild the EDA-aware knowledge graph as organizational memory: entity and relationship inference, ontology evolution, versioning, and temporal queries.\nImplement agentic memory and outbound MCP servers exposing retrieval, graph traversal, and lineage to external agents with access controls gatekeeping and full audit.\nEngineer governance so access control propagates from source data through embeddings, graph nodes, retrievals, and agent responses.\nBenchmark retrieval quality, embedding models, and LLMs against EDA use cases, selecting models per task under cost and latency constraints.\nCollaborate with product, EDA tool teams and customers to translate semiconductor and RF workflows into requirements.\nQualifications\nMS or PhD in Computer Science, Electrical Engineering, or related field\n5+ years building production ML or data-intensive systems.\nDemonstrated experience building RAG and knowledge graph systems in production (a must): ingestion, indexing, and retrieval pipelines.\nHands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.\nStrong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.\nExperience with agentic memory management and the Model Context Protocol (MCP) or comparable agent-grounding interfaces.\nProficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.\nML applied to semiconductor applications, especially the RF and microwave industry, is highly valued.\nFamiliarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.\nCareers Privacy Statement ***Keysight is an Equal Opportunity Employer.***\nDesign and build low-latency hybrid retrieval (lexical, vector, graph, faceted) over large, heterogeneous data for on-premises, IP-sensitive deployments.\nDevelop the semantic insight layer: automated tagging, domain-aware metadata, and embeddings as first-class managed assets with version provenance.\nBuild the EDA-aware knowledge graph as organizational memory: entity and relationship inference, ontology evolution, versioning, and temporal queries.\nImplement agentic memory and outbound MCP servers exposing retrieval, graph traversal, and lineage to external agents with access controls gatekeeping and full audit.\nEngineer governance so access control propagates from source data through embeddings, graph nodes, retrievals, and agent responses.\nBenchmark retrieval quality, embedding models, and LLMs against EDA use cases, selecting models per task under cost and latency constraints.\nCollaborate with product, EDA tool teams and customers to translate semiconductor and RF workflows into requirements.\nMS or PhD in Computer Science, Electrical Engineering, or related field\n5+ years building production ML or data-intensive systems.\nDemonstrated experience building RAG and knowledge graph systems in production (a must): ingestion, indexing, and retrieval pipelines.\nHands-on expertise with LLMs: embeddings, fine-tuning, prompt and context engineering, evaluation, and open-weights models for on-prem inference.\nStrong command of vector databases, graph databases, and low-latency retrieval infrastructure at scale.\nExperience with agentic memory management and the Model Context Protocol (MCP) or comparable agent-grounding interfaces.\nProficiency in Python and modern ML frameworks (PyTorch, Hugging Face, etc..), with solid software engineering and API design practices.\nML applied to semiconductor applications, especially the RF and microwave industry, is highly valued.\nFamiliarity with data governance, access control, and provenance in IP-sensitive or regulated environments is a plus.\nCareers Privacy Statement ***Keysight is an Equal Opportunity Employer.***","description_format":"text","description_chars":5178,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"master","optional":false},"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Artificial Intelligence","Simulation & Digital Twin Software","Custom Software 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