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About This Role
Help empower our global customers to connect to culture through their passions.
Why you'll love this role
Join a team of ML Engineers and Applied Scientists shaping how millions of buyers and sellers experience StockX. From finding the right product to detecting counterfeits, you'll tackle new challenges across our Live, Listings, and Core businesses—and turn advances in AI/ML into real marketplace impact.
Reporting to the VP of Engineering, you'll set the AI/ML engineering strategy, build and develop the team, and make the decisions that move our highest-impact opportunities forward. You'll partner with Product, Data Science, and business leaders to decide where we invest, what we build, and how we measure success.
What you'll do
- Own the AI/ML engineering strategy and roadmap, connecting technical investments to customer needs and business priorities.
- Build and develop a high-performing team of ML engineers and applied scientists, growing technical leaders and managers as the organization scales.
- Lead delivery across search, recommendations, pricing, forecasting, fraud detection, and counterfeit detection.
- Set technical direction, guide architecture and model decisions, and choose the right approach—from classical ML to generative AI.
- Drive rigorous experimentation and A/B testing to measure impact and focus resources on what works.
- Scale ML platforms and MLOps practices that help teams ship quickly, reliably, and cost-effectively.
- Own hiring, performance, resource planning, and investment tradeoffs; align senior stakeholders on priorities and results.
About you
- 10+ years of relevant, hands-on experience in ML engineering or applied science, with a track record of building and operating production ML systems.
- 7+ years of practical ML or data science experience, including experience in computer vision, multi-modal learning, search, or recommendation systems.
- 5+ years of engineering leadership experience, including hiring, developing talent, and leading multiple complex workstreams.
- Experience owning an AI/ML roadmap and delivering measurable customer or business outcomes through cross-functional partnerships.
- Strong technical judgment across model design, architecture, experimentation, and cloud infrastructure, with the ability to balance performance, speed, and cost.
- Clear, compelling communication and the ability to influence senior leaders, make tough prioritization decisions, and turn ambiguity into action.
Nice to have skills
- Experience managing managers or leading multiple ML engineering or applied science teams.
- Marketplace, e-commerce, or Supply Chain experience, particularly in search, recommendations, computer vision, or fraud and trust and safety.
- Advanced degree in Computer Science, Machine Learning, or a related field; experience with AWS and shared ML platforms.
Pursuant to the Colorado Fair Pay Act, the base salary range in Colorado for this position is $225,000 to $275,000, plus opportunities for benefits (e.g., medical, dental), equity and discretionary bonuses. Compensation in other geographies may vary.
Pursuant to the San Francisco Fair Chance Ordinance, Los Angeles Fair Chance Initiative for Hiring Ordinance, and any other state or local hiring regulations, we will consider for employment any qualified applicant, including those with arrest and conviction records, in a manner consistent with the applicable regulation.
About StockX
StockX is proud to be a Detroit-based technology leader focused on the large and growing online market for sneakers, apparel, accessories, electronics, collectibles, trading cards, and more. StockX's powerful platform connects buyers and sellers of high-demand consumer goods from around the world using dynamic pricing mechanics. This approach affords access and market visibility powered by real-time data that empowers buyers and sellers to determine and transact based on market value. The StockX platform features hundreds of brands across verticals including Jordan Brand, adidas, Nike, Supreme, BAPE, Off-White, Louis Vuitton, Gucci; collectibles from brands including LEGO, KAWS, Bearbrick, and Pop Mart; and electronics from industry-leading manufacturers Sony, Microsoft, Meta, and Apple. Launched in 2016, StockX employs 1,000 people across offices and verification centers around the world. Learn more at www.stockx.com.
We are an equal opportunity employer and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. This job description is intended to convey information essential to understanding the scope of the job and the general nature and level of work performed by job holders within this job. However, this job description is not intended to be an exhaustive list of qualifications, skills, efforts, duties, responsibilities or working conditions associated with the position. StockX reserves the right to amend this job description at any time. StockX may utilize AI to rank job applicant submissions against the position requirements to assist in determining candidate alignment.
Salary Context
This $225K-$275K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At StockX, 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. Director-level AI roles across all categories have a median of $272,300. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $225K to $275K.
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.
StockX AI Hiring
StockX has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $275K - $275K.
Location Context
AI roles in New York pay a median of $221,000 across 3,078 tracked positions. That's 4% 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 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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