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About This Role
At Five Below our growth is a result of the people who embrace our purpose: We know life is way better when you are free to Let Go \& Have Fun in an amazing experience, filled with unlimited possibilities, priced so low, you can always say yes to the newest, coolest stuff! Just ask any of our over 27,000 associates who work at Five Below and they’ll tell you there’s no other place like it. It all starts with our purpose and then, The Five Below Way, which is our values and behaviors that each and every associate believes in.
It’s all about culture at Five Below, making this a place that can inspire you as much as you inspire us with big ideas, super energy, passion, and the ability to make the workplace a WOWplace!
Key Responsibilities
1\. AI Architecture \& Strategy
- Define and own the enterprise AI architecture for retail use cases, aligning with business priorities and technology strategy.
- Develop reference architectures, patterns, and standards for AI/ML and Generative AI solutions, with an emphasis on open\-source\-first design principles.
- Translate retail business problems — across merchandising, supply chain, stores, marketing, and e\-commerce — into scalable AI solution blueprints.
- Partner with business and product leaders to identify and prioritize high\-impact AI opportunities.
- Champion open\-source AI frameworks and tooling (e.g., Hugging Face, LangChain, LlamaIndex, Ray, MLflow, Feast) as the default approach before evaluating commercial alternatives.
2\. Open\-Source AI Architecture
- Lead the selection, evaluation, and integration of open\-source AI and ML frameworks into Company’s enterprise architecture.
- Design reusable patterns for open\-source LLM deployment, fine\-tuning, and serving (e.g., vLLM, Ollama, llama.cpp, OpenLLM).
- Establish governance standards for open\-source model usage, including licensing review, security scanning, and model provenance tracking.
- Build internal capability around open\-source foundations to reduce vendor lock\-in and accelerate experimentation velocity.
- Evaluate and adopt emerging open\-source agentic frameworks (e.g., AutoGen, CrewAI, LangGraph) for retail automation use cases.
3\. AI Solutioning \& Design
- Architect end\-to\-end AI solutions, including data ingestion, feature engineering, model training, inference, and system integration.
- Design AI systems for core retail domains such as:
- Search, recommendations, and personalization
- Demand forecasting, inventory optimization, replenishment, and allocation
- Pricing and markdown optimization
- AI assistants and copilots for store, merchandising, and supply\-chain teams
- Define integration patterns between AI services and retail platforms (POS, OMS, WMS, CRM, e\-commerce).
- Lead architectural reviews, ensuring solutions meet performance, scalability, security, cost, and reliability requirements.
4\. AI Observability
- Define and implement an AI observability framework covering model performance monitoring, data drift detection, prediction quality tracking, and system health across all production AI systems.
- Establish real\-time and batch monitoring pipelines for model inference using tools such as Evidently AI, Arize, WhyLogs, Fiddler, or equivalent open\-source platforms.
- Design standardized dashboards and alerting for model degradation, data skew, latency SLO breaches, and feature store anomalies.
- Build feedback loop infrastructure to capture ground\-truth labels and enable continuous model evaluation in production.
- Define observability standards for GenAI and LLM systems, including hallucination rate tracking, prompt/response logging, latency percentiles, and cost\-per\-query attribution.
- Partner with MLOps and Platform Engineering to embed observability as a first\-class requirement in every AI system from Day 1\.
5\. AI Security
- Serve as the AI security authority for Company, owning the threat model for all AI and ML systems in production.
- Define and enforce secure\-by\-design standards for model development, training data handling, inference APIs, and GenAI integrations.
- Architect defenses against AI\-specific attack vectors, including prompt injection, model inversion, adversarial inputs, data poisoning, and supply chain risks in open\-source model adoption.
- Establish data privacy controls for AI pipelines, ensuring compliance with applicable regulations (e.g., CCPA) and internal data governance policies.
- Lead AI red\-teaming and adversarial testing exercises to proactively identify and remediate security gaps before production deployment.
- Partner with Information Security, Legal, and Enterprise Risk to maintain an AI risk register and align AI security posture with the organization’s broader cybersecurity framework.
- Define guardrails, content filtering, and human\-in\-the\-loop safeguards for all customer\-facing and associate\-facing GenAI applications.
6\. MLOps, GenAI \& Governance
- Establish MLOps and AIOps practices, including CI/CD for models, automated retraining, monitoring, drift detection, and cost controls.
- Define standards for Generative AI and LLM usage, including multi\-RAG architectures, MCP, and vector search.
- Define prompt orchestration, tool\-calling, and agentic workflow patterns.
- Ensure AI solutions comply with data privacy, security, and responsible AI principles.
- Partner with Security, Legal, and Enterprise Architecture to align AI solutions with governance and risk standards.
7\. AI Productivity Tooling Mandate
- Personally mandate and model the daily use of AI\-native productivity tools across all architecture and delivery work.
- Evaluate, recommend, and govern the enterprise use of tools including:
- Microsoft Copilot – for productivity, code assistance, and enterprise knowledge retrieval
- Cursor – for AI\-assisted development and code generation within engineering workflows
- Glean – for enterprise search, institutional knowledge management, and AI\-powered information retrieval
- Claude (Anthropic) – for complex reasoning, document synthesis, and agentic task automation
- Equivalent or emerging AI productivity platforms as the market evolves
- Define standards and guardrails for enterprise AI tool adoption, including data classification policies governing what information may be shared with each platform.
- Train and upskill engineering and cross\-functional teams on effective use of AI productivity tooling to multiply output and reduce time\-to\-delivery.
8\. Technology Evaluation, Implementation \& Delivery
- Work closely with AI Engineers, ML Engineers, Data Engineers, and platform teams to ensure architectures are production\-ready and executable.
- Provide hands\-on guidance during implementation, including reference code, pipelines, schemas, and infrastructure patterns.
- Evaluate and recommend AI SaaS solutions, cloud services, and frameworks (AWS, Azure, GCP, Databricks, Snowflake, etc.).
- Lead build vs. buy vs. open\-source decisions and support vendor selection for AI capabilities.
Required Qualifications
- 9\+ years of experience in software, data, or AI engineering, with 5\+ years in AI/ML architecture roles.
- Proven experience designing and delivering production AI solutions specifically in retail, e\-commerce, supply chain, or consumer\-facing industries — this is a non\-negotiable requirement.
- Deep hands\-on expertise with open\-source AI/ML ecosystem: Hugging Face Transformers, LangChain, LlamaIndex, MLflow, Ray, Feast, Evidently, or equivalent.
- Strong proficiency in Python and ML frameworks (PyTorch, TensorFlow, scikit\-learn).
- Experience with modern data architectures: lakehouse, streaming, batch pipelines; platforms such as Databricks and Snowflake.
- Demonstrated experience designing AI observability systems — including model monitoring, drift detection, and production feedback loops.
- Working knowledge of AI security threat models, including prompt injection, adversarial attacks, and secure LLM deployment practices.
- Hands\-on experience with cloud platforms and managed AI/ML services (AWS SageMaker, Azure ML, Vertex AI, or equivalent).
- Established practice of using AI productivity tools (e.g., Copilot, Cursor, Claude, Glean, or similar) in daily engineering and architecture work.
- Excellent communication skills with the ability to explain complex architectures to both technical and business stakeholders.
Preferred Qualifications
- Experience building or scaling enterprise AI platforms or AI Centers of Excellence.
- Contributions to open\-source AI projects or published architecture patterns.
- Experience with AI red\-teaming, adversarial testing, or formal AI risk assessment frameworks.
- Familiarity with retail\-specific platforms: Manhattan WMS, Blue Yonder, Aptos POS, Salesforce Commerce Cloud, or equivalent.
- Cloud or AI certifications (AWS ML Specialty, Azure AI Engineer, GCP Professional ML Engineer).
Explore our benefits site to discover all the perks and support we offer! From health coverage to financial and personal wellness, we've got you covered—check it out today! benefits.fivebelow.com/public/welcome
Five Below is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state, or local laws.
Five Below is committed to working with and providing reasonable accommodations for individuals with disabilities. If you need a reasonable accommodation because of a disability for any part of the employment process, please submit a request and let us know the nature of your request and your contact information. crewservices.zendesk.com/hc/en\-us/requests/new
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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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% of the market. At Five Below, 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 $178,940 based on 11,900 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $160,000.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Five Below AI Hiring
Five Below has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Architect. Based in Philadelphia, PA, US.
Location Context
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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