Principal AI/ML Engineer, Semantic Data

Job Summary

Major 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.

Responsibilities

• Design and implement embedding pipelines across fan data, content, metadata, and behavioral signals
• Build metadata and enrichment systems that normalize and structure enterprise data for AI use
• Develop knowledge bases and retrieval systems using vector databases and hybrid search architectures
• Create context assembly pipelines combining structured data, documents, APIs, and historical outputs
• Enable AI systems to operate on unified semantic representations rather than raw data
• Architect and manage knowledge graphs representing fan, content, and business entity relationships
• Define and maintain a semantic layer standardizing metrics, features, and business concepts
• Implement graph-based reasoning and enrichment workflows
• Ensure semantic consistency across analytics, ML, and operational systems
• Design and build retrieval-augmented generation (RAG) systems grounded in semantic data
• Integrate LLMs for reasoning over structured and unstructured data
• Develop pipelines translating natural language into structured outputs such as queries and analytical tasks
• Build and optimize context pipelines improving LLM grounding and factual accuracy
• Evaluate and integrate open-weight models for domain-specific reasoning
• Fine-tune or adapt models using parameter-efficient techniques
• Support deployment of LLM systems in private or on-prem GPU environments
• Optimize inference workflows for latency, cost, and scalability
• Enable LLM-driven workflows that reason over semantic data and retrieval systems
• Build scalable, production-grade services and APIs for semantic and AI systems
• Work with vector and graph databases to support retrieval and reasoning
• Integrate structured data, documents, APIs, and model outputs
• Partner with data engineering on batch and real-time pipelines
• Ensure systems meet performance and reliability requirements
• Design evaluation frameworks for retrieval quality and LLM output correctness
• Monitor system performance, relevance, and model behavior
• Establish guardrails for explainability, traceability, and data attribution
• Mitigate risks related to bias, data leakage, and inconsistencies
• Collaborate with product, analytics, and engineering teams on AI use cases
• Translate business problems into systems combining semantic data and LLM reasoning
• Partner with ML teams to improve model performance through better grounding
• Mentor engineers and establish best practices

Requirements

• Master’s degree or higher in computer science, engineering, or related field, or equivalent experience
• 8-10+ years of experience in ML engineering, data systems, or applied AI
• Strong expertise in Python, SQL, and production software engineering
• Deep experience with semantic data modeling, ontologies, and entity resolution
• Hands-on experience with embeddings, vector search, and retrieval systems
• Experience building and deploying LLM-powered systems including RAG
• Experience building production-grade AI systems at scale
• Strong understanding of distributed systems and data architecture

Qualifications

• Experience with knowledge graphs and graph databases
• Experience designing semantic layers or feature stores
• Experience with open-weight LLMs and model adaptation
• Familiarity with on-prem or private GPU deployments
• Experience with modern data platforms (AWS, Snowflake, Databricks)

Benefits

Total Rewards package includes comprehensive medical, dental, and vision coverage, a $500 wellness reimbursement, and generous Holiday and PTO schedule to promote work-life balance. Career and professional development opportunities are also available, including on-the-job training, feedback, and ongoing educational opportunities. Major League Soccer is committed to providing a safe and inclusive environment for all employees.