Senior Applied Machine Learning Engineer

Shopify

About The Role

Shopify is building SimGym, an AI-powered store testing system that simulates buyer behavior using browser automation and LLM/VLM agents. SimGym helps merchants understand how real shoppers might experience their storefronts, then turns those simulations into trustworthy, merchant-facing theme insights and recommendations.

We are looking for a Senior Applied Machine Learning Engineer to lead high-value ML work within SimGym. This is a hands-on technical role for someone who can execute through ambiguity, write production-quality code, make strong technical decisions, and help turn model experiments into reliable systems with real merchant impact.

You will work on problems across multimodal browsing agents, teacher-model distillation, learned buyer personas, SFT/post-training, evaluation systems, behavioral data pipelines, and production rollout infrastructure. The goal is not just to improve benchmarks, but to help build AI agents that behave more like real buyers and produce recommendations merchants can trust.

What You'll Do

• Lead high-impact ML projects within SimGym's agent, persona, evaluation, or data flywheel workstreams.

• Develop, fine-tune, evaluate, and deploy LLM/VLM-based systems for browser automation, buyer simulation, and storefront analysis.

• Move model improvements from experiment toward production readiness, such as VLM teacher distillation, learned buyer personas, or SFT model rollout.

• Design and build scalable ML components, data pipelines, and model-serving paths that are easy to understand, operate, and maintain.

• Improve human-vs-agent alignment and behavioral fidelity through strong offline and online evaluation practices.

• Contribute to golden datasets, trace replay systems, screenshot/A11y-tree evaluation flows, and reliable benchmark infrastructure.

• Use Shopify-scale behavioral and storefront data to improve buyer simulation, personalization, and merchant-facing recommendations.

• Diagnose complex model, data, and system failures with attention to the important details.

• Make practical technical tradeoffs across models, infrastructure, evaluation tooling, and data systems.

• Communicate clearly with engineering, product, data, research, and partner teams.

• Help raise the technical bar for the team through strong execution, thoughtful design, code quality, and mentorship by example.

What You'll Need

Strong applied ML engineering experience, with a track record of delivering production systems through ambiguous technical problems.

Hands-on experience with LLM, VLM, or agent systems, including SFT/post-training, distillation, evaluation, model iteration, or serving tradeoffs.

End-to-end experience training, evaluating, testing, deploying, and operating ML products at meaningful scale.

Strong experimentation and evaluation instincts, including designing useful metrics, diagnosing alignment failures, and avoiding misleading benchmark wins.

Experience building production ML data pipelines and working with large-scale behavioral, event, or product data.

Proficiency with Python, shell scripting, batch and streaming data pipelines, orchestration tools, vector databases, BigQuery/BigTable or equivalent systems.

Experience working with parallel or distributed ML environments, including GPU optimization, model serving, or large-scale training/inference workflows.

Strong software engineering fundamentals: code quality, operational awareness, maintainability, and speed of execution.

Clear communication with both technical and non-technical audiences.

Nice To Have

Experience with browser automation, AI agents, or simulated user behavior.

Background in e-commerce, search, recommendations, personalization, or buyer behavior modeling.

Experience with sequence or behavior representation learning, recommender embeddings, HSTU-style architectures, vLLM/GPU serving, Spark/GCS/Hugging Face-style data workflows.

Prior experience translating research prototypes into reliable, production user-facing systems.

Why This Role

This is a high-leverage role in Shopify's applied AI work for merchants. You will help build the ML systems behind SimGym while working on frontier LLM/VLM agents, learned personas, evaluation science, and production systems with direct merchant impact.

This role may require on-call work. Shopify's hiring process moves quickly, and candidates should be prepared for a technical interview loop that includes pair programming using their own IDE.

About the discipline

Engineering. It’s our DNA.

Engineering isn't a department at Shopify. It's our DNA. The engineers who thrive here care about the code, the system, and the merchants relying on both. They want hard problems and uncomfortable growth. They find solutions without waiting for permission. They ship weekly. They want commerce to work better for everyone. So does our founder, Tobi, who still codes and still shapes where this thing is going.

They're open source nerds. They pair program because two brains write better code than one. New tools, new models, new approaches. They experiment constantly. Most of what's standard here started as someone hacking on something.

They use AI like a keyboard, reflexively and everywhere, to clear the mundane and make room for thinking. And they work in the open. The fastest way to learn here is to watch someone better than you do the work. If that sounds like you, come build with us.

Infrastructure engineering

Infrastructure Engineers at Shopify build and maintain the systems that handle millions of requests per second and billions of dollars in transactions. At that scale, performance gains matter and weak assumptions surface quickly. Your systems power the experience for millions of entrepreneurs and buyers every minute of every day, including peak events like Black Friday.

Engineers at Shopify spend the majority of their time actually writing code to build and improve infrastructure. You’ll solve problems that don’t exist anywhere else, with challenges that push what’s possible. If that lights you up, come build with us.

Growth and rewards

Mastery-matched compensation.

~Mastery

At Shopify, getting better at your craft and increasing your impact is how you grow. Twice a year, we assess both in our ~Mastery cycle, Shopify’s performance review. Achieving higher ~Mastery is a promotion, and when you earn a promotion your total rewards grow with it. Crafter and manager are equal paths, so you can pursue excellence in the craft you’re best at.

Total Rewards

With Flex Comp, your Rewards Wallet gives you agency over your compensation mix. Adjust your mix of cash, restricted share units, and stock options (including programs like Long-Term Equity)* to where it makes sense for you, and mix it up anytime as life and priorities change. Buying a house? Want more skin in the game? Just experimenting? It’s your call.

• Flex Comp program offerings vary based on location, role, and employment type. Full details are set out in our Terms & Conditions and related plan documents.

Built for real life

Room for the rest of your life.

Flexible work, remote by design

A flexible vacation policy means most Shopifolk can take the vacation days they feel they need to recharge.

If a change of everyday scenery is more your jam, Destination 90 means you can work from (almost) anywhere in the world for 90 days of the year.

Your setup

Your battlestation is your home base. Request the desk, chair, and AV gear you need to get shit done.

Benefits

• You need ‘em, we’ve got ‘em. Our health and welfare benefits may include health, dental, vision, disability, and life insurance depending on your country. If you’re in a country without a package, you'll receive a monthly allowance. We also offer mental health support for you and your dependents, including immediate and confidential services like counseling and coaching sessions.

• Family support

• We offer fertility benefits globally, along with paid parental leave to welcome a new family member.