Principal AI/ML Engineer, Semantic Data

OverviewMajor League Soccer is building advanced AI and data platforms to power fan intelligence, personalization, and data-driven decisioning across the organization.The Principal AI/ML Engineer, Semantic Data will design and build the semantic intelligence layer that enables consistent understanding of fan data, business concepts, and operational workflows across MLS systems.This role combines semantic data systems with applied LLM engineering to build grounded, production-grade AI capabilities.This is a systems engineering role responsible for building and scaling real-world AI infrastructure, including knowledge graphs, retrieval systems, and LLM-powered applications.ResponsibilitiesAI & Knowledge Systems DevelopmentDesign and implement embedding pipelines across fan data, content, metadata, and behavioral signalsBuild metadata and enrichment systems that normalize and structure enterprise data for AI useDevelop knowledge bases and retrieval systems using vector databases and hybrid search architecturesCreate context assembly pipelines combining structured data, documents, APIs, and historical outputsEnable AI systems to operate on unified semantic representations rather than raw dataSemantic Layer & Knowledge GraphsArchitect and manage knowledge graphs representing fan, content, and business entity relationshipsDefine and maintain a semantic layer standardizing metrics, features, and business conceptsDesign ontologies, taxonomies, and entity models for fan behavior and identityImplement graph-based reasoning and enrichment workflowsEnsure semantic consistency across analytics, ML, and operational systemsLLM & Applied AI SystemsDesign and build retrieval-augmented generation (RAG) systems grounded in semantic dataIntegrate LLMs for reasoning over structured and unstructured dataDevelop pipelines translating natural language into structured outputs such as queries and analytical tasksBuild and optimize context pipelines improving LLM grounding and factual accuracyEvaluate and integrate open-weight models for domain-specific reasoningFine-tune or adapt models using parameter-efficient techniquesSupport deployment of LLM systems in private or on-prem GPU environmentsOptimize inference workflows for latency, cost, and scalabilityEnable LLM-driven workflows that reason over semantic data and retrieval systemsPlatform & InfrastructureBuild scalable, production-grade services and APIs for semantic and AI systemsWork with vector and graph databases to support retrieval and reasoningIntegrate structured data, documents, APIs, and model outputsPartner with data engineering on batch and real-time pipelinesEnsure systems meet performance and reliability requirementsGovernance, Evaluation & ReliabilityDesign evaluation frameworks for retrieval quality and LLM output correctnessMonitor system performance, relevance, and model behaviorEstablish guardrails for explainability, traceability, and data attributionEnsure safe and reliable generation of structured outputsMitigate risks related to bias, data leakage, and inconsistenciesCross-Functional CollaborationCollaborate with product, analytics, and engineering teams on AI use casesTranslate business problems into systems combining semantic data and LLM reasoningPartner with ML teams to improve model performance through better groundingMentor engineers and establish best practicesQualificationsMaster’s degree or higher in computer science, engineering, or related field, or equivalent experience8–10+ years of experience in ML engineering, data systems, or applied AIStrong expertise in Python, SQL, and production software engineeringDeep experience with semantic data modeling, ontologies, and entity resolutionHands-on experience with embeddings, vector search, and retrieval systemsExperience building and deploying LLM-powered systems including RAGExperience building production-grade AI systems at scaleStrong understanding of distributed systems and data architecturePreferred QualificationsExperience with knowledge graphs and graph databasesExperience designing semantic layers or feature storesExperience with open-weight LLMs and model adaptationFamiliarity with on-prem or private GPU deploymentsExperience with modern data platforms (AWS, Snowflake, Databricks)Background in marketing analytics, personalization, or customer data platformsTotal RewardsMajor League Soccer offers a competitive starting base salary of $235,000-$260,000, based on individual qualifications, market financials, and operational business needs. We are committed to providing a Total Rewards package that attracts, supports, engages, and retains talent. Our benefits package includes comprehensive medical, dental, and vision coverage, a $500 wellness reimbursement, and generous Holiday and PTO schedule to promote work-life balance. We also prioritize career and professional development, offering on-the-job training, feedback, and ongoing educational opportunities.Major League Soccer believes in the value of in-person collaboration to support teamwork, creativity, and connection. Employees in this role are expected to work a four (4) day in-office schedule, with the flexibility to work remotely one (1) day each week, based on business and department needs.Major League Soccer is an equal opportunity employer. Employment decisions are made without regard to race, color, religion, sex, sexual orientation, gender identity or expression, pregnancy, age, national origin, disability, genetic information, protected veteran status, or any other characteristic protected by applicable federal, state, or local law. Major League Soccer is committed to providing reasonable accommodations to individuals with disabilities throughout the application and hiring process, as well as during employment. Applicants who require an accommodation may contact Human Resources to request assistance.Join our team and help support the growth and success of Major League Soccer. Job SummaryID: 2026-2291# of Openings: 1Category: Data Innovation & Engineering