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About This Role
The AI Enablement Engineer helps teams across Topgolf, technical and non-technical alike, get real value from AI by removing the friction that blocks adoption and building the reusable archetypes, skills, and governed integrations that let business teams build safe, working applications through Topgolf’s AI-SDLC pipeline. This role acts as the technical backstop, reviewing AI-generated code and specs for correctness, security, and adherence to engineering standards before anything ships.
- Partner with teams across Topgolf, technical and non-technical, to unblock AI adoption by configuring agent access, resolving friction, and showing teams how to get real value from available AI tools
- Design and build archetypes and skills (e.g., SKILL.md files, intake questionnaires, spec templates, code scaffolds) that package engineering standards and governance into reusable building blocks for the AI-SDLC pipeline
- Connect agents to internal systems, data sources, and tools through governed connectors and MCP-style integrations
- Monitor archetypes and agents for model drift, prompt bloat, and other degradation as underlying models and usage evolve, and keep code scaffolds and prompts current with secure coding practices and platform engineering standards
- Review AI agent-produced code, specs, and gate results for correctness, security, scope adherence, and good development practice before production
- Test archetypes and agents against adversarial prompting (prompt injection, jailbreak attempts) and implement guardrail hooks to enforce scope, safety, and policy compliance
- Build documentation, examples, and light training (office hours, walkthroughs, guides) to raise teams' baseline capability with AI
- Retire, merge, or extend archetypes based on usage and escalation patterns, and triage escalations to security, data owners, or app stewards, partnering with platform engineering so new archetypes and integrations fit the pipeline's runtime and release mechanics
- Triage escalations and route findings to security, data owners, or app stewards, following the pipeline's escalation rules.
- Partner with the platform engineering team so new archetypes and integrations fit the pipeline's constrained runtime, connector catalog, and release mechanics.
Required Skills and Experience
- Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.
- 3+ years of professional software engineering experience, with strong proficiency in Python and working knowledge of at least one other widely used language such as Go, TypeScript, or Java.
- Strong grounding in core software development principles: automated testing, version control, code review, secure coding practices, and API design.
- Hands on experience with LLM agent tooling, for example Claude Code, MCP, LangGraph, or a comparable framework, including prompting, tool use, and agent orchestration.
- Experience with retrieval-augmented generation (RAG) and vector databases, and judgment for when they are the right tool versus a simpler approach.
- Ability to evaluate AI generated code for correctness and risk, and to explain technical tradeoffs to non-technical stakeholders in plain language.
- Experience designing templates, documentation, or tooling that other engineers or non-experts build from.
Comfort operating as both a broad enabler, unblocking adoption across many teams, and a careful reviewer, gating what individual teams ship.
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Preferred Qualifications
- Experience setting up and administering AI coding tools or agent platforms, such as Claude Code, GitHub Copilot, or Cursor, across a team or company.
- Familiarity with the Claude Agent SDK, Model Context Protocol (MCP), or comparable agent frameworks such as LangGraph, CrewAI, or AutoGen.
- Experience building internal AI adoption programs: office hours, documentation, champion networks, or similar.
- Background in code review, static analysis, or security review, enough to interpret gate findings without necessarily building the scanners.
Experience contributing to governance, approval, or escalation workflows for a technical platform.
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What Success Looks Like
- Teams across Topgolf, not just engineering, are visibly getting more done with AI than they were a quarter ago, and can point to you as part of why.
- Business teams build safe, working applications inside the pipeline instead of routing every idea to engineering.
- The archetypes you maintain stay current, well scoped, and easy for non-technical builders to use without guessing.
- What ships has already been checked against good software development practice, because you caught what the automated gates could not.
- The archetype catalog and the tools you connect evolve based on evidence: what teams actually need, and what keeps showing up in questions and escalations.
BENEFITS
Free Play & 1/2 price food! Health, dental, vision, 401(k) team member match, free mental well-being platform – and that’s just for starters for those who qualify.
ADA
The above statements cover what are generally believed to be principal and essential functions of the job. Specific circumstances may allow or require some incumbents assigned to the job to perform a different combination of duties.
EEO Statement
Topgolf is a global sports and entertainment community and is committed to equal opportunity and is firmly committed to preventing discrimination and harassment, including sexual misconduct, based on legally protected diversity characteristics (such as race, color, religion, national origin, sex, age, disability, sexual orientation, gender identity or expression, family status, citizenship, genetic information and veteran status) in its application and hiring processes and in its employment decisions. As an affirmative action employer, Topgolf also takes steps to prevent retaliation and to create a respectful, equitable and inclusive environment for our Guests, Associates, business partners, vendors, and the communities we serve.
Topgolf is an equal opportunity affirmative action employer and administers all personnel practices without regard to race, color, religion, sex, age, national origin, disability, sexual orientation, gender identity or expression, marital status, veteran status, genetics or any other category protected under applicable law.
Topgolf Entertainment Group does not accept unsolicited agency resumes. Please do not forward unsolicited agency resumes to our website, employees or Human Resources. Topgolf will not pay fees for unsolicited agency resumes and will not be responsible for any agency fees associated with unsolicited resumes. Unsolicited resumes received will be considered property of Topgolf Entertainment Group and will be processed accordingly without fee.
Topgolf participates in E-Verify and will provide the federal government with your Form I-9 information to confirm that you are authorized to work in the U.S.
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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Topgolf, 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Topgolf AI Hiring
Topgolf has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Dallas, TX, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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