Overview
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.
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.
Responsibilities
AI & Knowledge Systems Development
- 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
Semantic Layer & Knowledge Graphs
- Architect and manage knowledge graphs representing fan, content, and business entity relationships
- Define and maintain a semantic layer standardizing metrics, features, and business concepts
- Design ontologies, taxonomies, and entity models for fan behavior and identity
- Implement graph-based reasoning and enrichment workflows
- Ensure semantic consistency across analytics, ML, and operational systems
LLM & Applied AI 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
Platform & Infrastructure
- 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
Governance, Evaluation & Reliability
- 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
- Ensure safe and reliable generation of structured outputs
- Mitigate risks related to bias, data leakage, and inconsistencies
Cross-Functional Collaboration
- 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
Qualifications
- Master’s degree or higher in computer science, engineering, or related field
- 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
Preferred 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)
- Background in marketing analytics, personalization, or customer data platforms
Total Rewards
Major League Soccer offers a competitive starting base salary of $200,000-$235,000, 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.