Interested in this AI/ML Engineer role at Spirit Halloween?
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Overview:
We are authentic, professional providers of fun, focused on building a Great Place To Work For All by staying true to our mission: “Life’s a Party, We’re Makin’ It Fun!” and “So Much Fun It’s Scary!”
At Spencer’s and Spirit, we do the right thing always-integrity, fairness, respect, and transparency are our foundation. You will find our culture to be inclusive, passionate, resilient, and one that values differences and embraces all.
One Team / One Goal
We are leaders and owners of our business success. Whether it’s developing new and exclusive costumes, quality testing products, or implementing technology solutions, our teams understand the value of working collaboratively to embrace change through innovation, curiosity, and thoughtfulness.
We offer a comprehensive benefits package that includes:
- Flexible work environment
- Career advancement
- Competitive base salary
- Bonus opportunity
- Vacation, Personal, Sick and Holiday pay
- Medical, Dental, Vision, Disability, Life and AD&D insurance
- 401k with a company match
- 30% merchandise discount
Responsibilities:
We're building AI into how our teams work, measured by business return.
In this role, you'll own the business-led AI portfolio: prioritizing where AI goes to work and making the case for each investment, in partnership with department leaders and IT.
You'll set the effort-and-value scoring method and guide teams through redesigning the workflows that matter most, improving decisions in sales, guest experience, and operations.
You'll prove the value case: setting the metrics that connect AI to dollars saved, hours returned, growth, profit, and customer loyalty; tracking ROI across the portfolio; and telling leadership what's working, what's next, and where to pivot.
You'll partner with IT within our responsible AI guardrails and guide a cross-functional network of department AI Champions, measuring success by adoption and speed to decision that drive business outcomes. This role reports up through the business. Key ResponsibilitiesAI Workflow Strategy & Value Realization* Develop and execute the AI enablement strategy, aligned to business priorities and technology roadmaps.
- Build and maintain the business-led AI portfolio of department goals, use cases, and workflows.
- Own the effort-and-value scoring method that decides which opportunities to prioritize and scale across sales, guest experience, and operations.
- Act as a business advisor and AI partner to department leaders, pushing innovation and adoption of high-value use cases across the enterprise.
- Define and communicate the metrics framework that ties AI investments to business return: dollars saved, hours returned, growth, profit impact, customer loyalty, and adoption.
- Lead the identification, design, and deployment of AI agents that automate workflows and extend the portfolio's business impact.
- Measure and track ROI across the portfolio, reporting progress, pivots, and recommendations to senior leadership.
Training & Capability Development
- Define AI fluency program frameworks, partnering with HR and Learning & Development to build programs, including our Learning Academy, that move teams from awareness to confident, everyday use of AI tools.
- Set and own enterprise AI fluency KPIs that track adoption and proficiency across departments and drive continuous improvement against those targets.
- Build and guide a network of AI Champions who drive adoption and surface new use cases within their departments.
Governance & Responsible AI* Serve as the business owner for responsible AI practices, ensuring initiatives align with company policies, governance standards, and business objectives.
- This role reports status and value metrics to the enterprise BI/AI Executive Team and reports up through the business to the SVP of Planning.
- Ensure quality of external AI partners and vendors, validating expertise and delivery alignment to the AI roadmap so solutions meet business needs.
- Maintain clear decision rights with IT: this role owns business-use-case approval and change adoption; IT retains final sign-off on data access, model risk, and security exceptions.
Qualifications:
- 10+ years across management consulting, business analytics, or business transformation, including 3+ years defining and scaling AI or advanced analytics initiatives at the enterprise level.
- Strong understanding of generative AI tools and technologies, including practical business applications, responsible AI practices, and the ability to translate technical capabilities into business outcomes.
- Experience with workflow mapping, process redesign, and prioritization frameworks.
- Experience moving technology or AI initiatives from pilot into sustained daily business use.
- Demonstrated success driving results, organizational change and adoption through influence rather than direct authority.
- Experience building business cases, defining success metrics, measuring ROI, and communicating outcomes to executive stakeholders.
- Strong communication, facilitation, stakeholder management, and executive presentation skills.
- Genuine enthusiasm for helping people work differently, building capability at scale, and keeping the human side of change moving alongside the technology.
- Retail, consumer products, multi-channel commerce, or other consumer-facing industry experience preferred.
- Organizational learning and enablement experience preferred.
- Bachelor's degree or equivalent experience.
The pay range reflects the potential base salary range for this role. Individual base pay is determined by various factors such as relevant experience and skills, and the scope and responsibilities of the position. Pay Range: $185,000 - $220,000
Salary Context
This $185K-$220K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 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 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Spirit Halloween, 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 in Demand for This Role
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 $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. Disclosed range: $185K to $220K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Spirit Halloween AI Hiring
Spirit Halloween has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Egg Harbor Township, NJ, US. Compensation range: $220K - $220K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 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 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 $253,100 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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