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About This Role
Remote \- Americas
Engineering \& Data
About the role
Machine Learning Infrastructure Engineers build and operate the end\-to\-end platform that powers AI—from data ingestion and training to large\-scale, low\-latency inference. They design high\-performance, GPU\-accelerated systems on Kubernetes, craft self\-serve developer experiences, and ship the paved roads that let ML teams move fast, safely, and at global scale. Some companies separate ML Infra, ML Platform and ML Ops\- at Shopify\- we call this ML Infrastructure. We have an agile workforce who can flex their experience and solve problems across these three domains.
Responsibilities:
Build and operate ML control planes, APIs, CLIs, SDKs, and self\-serve golden paths
Design and optimize multi\-tenant GPU Kubernetes clusters, including autoscaling, scheduling, packing, and utilization
Own model lifecycle: training orchestration/experiments, registries/versioning, CI/CD, canary/blue\-green, and safe rollback
Build real\-time serving stacks (KServe/Seldon/TensorFlow Serving) and end\-to\-end pipelines for batch and streaming
Design feature platforms and engineer storage/data movement for datasets, features, and artifacts tuned for cost/performance
Implement observability and SLOs across pipelines, training, and inference; automate remediation and capacity planning
Partner with ML, data, and product teams to unblock delivery and accelerate idea\-to\-impact
Qualifications:
Proven platform/infrastructure engineering experience with a track record of shipping production systems and code
Deep Kubernetes/containerization expertise for ML workloads (operators, Helm, service mesh/gRPC) and multi\-tenant clusters
Hands\-on experience running GPU infrastructure at scale (NVIDIA ecosystem; scheduling/packing/optimization)
Strong distributed systems and API/service design fundamentals; experience with high\-scale inference
Proficiency with infrastructure\-as\-code and automation (Terraform, Helm, GitOps) on major clouds (GCP/AWS/Azure)
Observability expertise (Prometheus/Grafana) and SLO\-driven operations for ML systems
Proficient in Python/Go/Java; experience building developer tooling and self\-service platforms
Nice to Haves:
Model serving and lifecycle tooling: KServe/Seldon/TensorFlow Serving, Kubeflow, MLflow/W\&B, model registries, DVC
Feature store experience (Feast/Tecton) with online/offline parity and SLAs
Data infrastructure familiarity (Kafka, Spark/Flink) and stateful stores (Redis/MySQL); CI/CD for online/batch inference
Model performance optimization (batching, caching, quantization, distillation) and hardware\-aware tuning
Experience with experimentation/A/B testing platforms and online evaluation frameworks
At Shopify, we pride ourselves on moving quickly—not just in shipping, but in our hiring process as well. If you're ready to apply, please be prepared to interview with us within the week. Our goal is to complete the entire interview loop within 30 days. You will be expected to complete a live pair programming session, come prepared with your own IDE.
This role may require on\-call work
About Shopify
Opportunity is not evenly distributed. Shopify puts independence within reach for anyone with a dream to start a business. We propel entrepreneurs and enterprises to scale the heights of their potential. Since 2006, we’ve grown to over 8,300 employees and generated over $1 trillion in sales for millions of merchants in 175 countries.
This is life\-defining work that directly impacts people’s lives as much as it transforms your own. This is putting the power of the few in the hands of the many, is a future with more voices rather than fewer, and is creating more choices instead of an elite option.
About you
Moving at our pace brings a lot of change, complexity, and ambiguity—and a little bit of chaos. Shopifolk thrive on that and are comfortable being uncomfortable. That means Shopify is not the right place for everyone.
Before you apply, consider if you can:
Care deeply about what you do and about making commerce better for everyone
Excel by seeking professional and personal hypergrowth
Keep up with an unrelenting pace (the week, not the quarter)
Be resilient and resourceful in face of ambiguity and thrive on (rather than endure) change
Bring critical thought and opinion
Put AI agents and tools to work on the tasks they're built for, and focus on the work only humans can do
Embrace differences and disagreement to get shit done and move forward
Work digital\-first for your daily work
We may use AI\-enabled tools to screen, select, and assess applications. All AI outputs are reviewed and validated by our recruitment team.
Shopifyhttps://www.shopify.com
We hire people, not resumes. If you think you’re right for the role, apply now.
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I haven't led a project yet, but I've contributed to the tasks assigned to me and learned from the process.
I've led a small project or a part of a larger project from start to finish.
I've led multiple projects across teams and stakeholders, and was accountable for the project's success.
I've led high\-stakes projects spanning multiple stakeholders, disciplines, and technical complexities.
Describe your involvement in the design and architecture of infrastructure or engineering projects in your current role.\*
I've implemented features based on existing designs and learned from the process.
I've contributed to the design and architecture of components or features within a larger application.
I've led the design and architecture of entire applications or significant parts of complex systems.
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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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At Shopify, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
Shopify AI Hiring
Shopify has 13 open AI roles right now. They're hiring across AI/ML Engineer, MLOps Engineer, Data Scientist, Research Scientist. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $156,000 across 1,221 positions. About 7% of all AI roles offer remote work.
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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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.
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