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About This Role
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
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- 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
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- 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
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- 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
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- 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
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- 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
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- 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, 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
Preferred Qualifications
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- 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 $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.
Salary Context
This $235K-$260K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Major League Soccer, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($247K) sits 37% above the category median. Disclosed range: $235K to $260K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Major League Soccer AI Hiring
Major League Soccer has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $260K - $260K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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