Shopify is the commerce platform that powers millions of merchants worldwide. Behind the product experience are ML systems that drive recommendations, search, and personalization at massive scale.

We build the compute and serving layer behind these systems: multi-node GPU training clusters, real-time inference with strict latency budgets, and the performance engineering that keeps it all efficient at scale. Our models serve hundreds of millions of buyers, and the infrastructure we build directly impacts how merchants grow their businesses.

The Role

You will own the core infrastructure that ML ML Engineers depend on to train and serve models: GPU training clusters, real-time serving systems, and the performance and reliability layer underneath both. You'll sit between ML Engineers who need fast iteration and production systems that need to stay up during events like Black Friday/Cyber Monday, where traffic and stakes peak simultaneously.

This role carries real technical authority. You'll make architectural decisions about how we scale training and serving, set standards for infrastructure quality, and be the person the team relies on when systems need to scale by an order of magnitude. You'll mentor engineers across the team, drive alignment on infrastructure direction across multiple workstreams, and influence technical strategy beyond your immediate team. You'll also raise the engineering bar through hiring and technical reviews.

What You'll Do

Training Infrastructure

• Design and operate GPU training pipelines on Kubernetes, including multi-node distributed training on GPU clusters
• Own training reliability: checkpointing, fault tolerance, preemption recovery, and resource scheduling
• Optimize training performance: mixed precision, kernel tuning, data loading throughput, and cluster utilization. You own compute efficiency; data correctness and freshness are owned by the operations side of the team.
• Build abstractions that let ML Engineers launch and iterate on training runs with minimal friction

Serving Infrastructure

• Build and maintain model serving infrastructure for real-time recommendation and LLM inference, with strict latency and throughput requirements
• Optimize serving cost and performance: batching strategies, model compilation, GPU right-sizing, and autoscaling
• Ensure serving systems meet availability and latency targets under peak traffic

Platform & Developer Experience

• Build internal tools and platforms that accelerate the model development lifecycle
• Define infrastructure patterns and best practices adopted across the team
• Improve the inner loop for ML Engineers: faster iteration from code change to training result to production evaluation

Technical Leadership

• Drive cross-team technical strategy for ML infrastructure - identify the next set of problems before they become blockers
• Mentor and up-level engineers on the team through pairing, design reviews, and setting technical standards
• Contribute to hiring: screen candidates, conduct technical interviews, and calibrate the engineering bar
• Write technical proposals and RFCs that shape infrastructure direction across the organization

What We're Looking For

Required

• 7+ years in software engineering, with 5+ years focused on ML infrastructure or distributed systems
• Deep hands-on experience with GPU training at scale: distributed training, checkpointing, fault recovery, and performance tuning. You've debugged real problems like NCCL hangs, gradient synchronization issues, or data loading bottlenecks.
• Strong Kubernetes skills: pod specs, GPU scheduling, resource quotas, debugging scheduling failures, and operating stateful GPU workloads
• Production model serving experience: you've built or operated serving systems behind real user traffic with latency constraints
• Solid Python and systems fundamentals; comfortable reading and modifying PyTorch training code
• Experience designing infrastructure abstractions used by other engineers
• Demonstrated technical leadership: you've driven architecture decisions, written technical proposals, and influenced engineering direction beyond your immediate team
• Track record of mentoring engineers and raising the technical bar on a team

Preferred

• Experience with cloud-native ML orchestration (SkyPilot, Ray, or similar)
• Hands-on with LLM serving stacks (vLLM, TensorRT-LLM, Triton, or equivalent)
• Experience with model compression in production (quantization, pruning, distillation)
• Experience operating recommendation or retrieval systems at scale
• Track record of building internal platforms adopted by other teams

How We Work

• You'll pair directly with ML ML Engineers. Understanding their models well enough to build the right infrastructure abstractions is part of the job.
• We prefer automation over runbooks. If a process can be scripted, it should be.
• On-call is shared. When you're on rotation, your scope is GPU cluster health, training failures, and serving availability - you own it end to end.
• You'll profile GPU kernels, chase p99 latency regressions, and care about FLOPS utilization. This is a deeply technical infrastructure role.
• Research and production are the same codebase. You'll see your infrastructure decisions reflected in real model quality and real merchant outcomes.
• Shopify operates on high trust and low process. You'll have real ownership and the autonomy to make decisions, not just execute tickets.

What Success Looks Like

• In 3 months: You've onboarded to training and serving infrastructure, shipped at least one meaningful improvement to reliability or performance, and can independently debug issues across the GPU stack.
• In 6 months: You own a major infrastructure subsystem (training cluster or serving platform). Researchers are training faster or serving more reliably because of changes you've made.
• In 12 months: You've shaped the technical roadmap for ML infrastructure and influenced engineering direction beyond the team. Other engineers across the organization come to you for architectural guidance. The platform scales to the next generation of models because of the systems and standards you've put in place. You've made the team stronger through hiring and mentorship.

Shopify is the commerce platform that powers millions of merchants worldwide. Behind the product experience are ML systems that drive recommendations, search, and personalization at massive scale.

We build and maintain the operational backbone behind these systems: deployment pipelines, evaluation frameworks, data preprocessing, and the monitoring that keeps models fresh and reliable in production. Our models serve hundreds of millions of buyers, and the pipelines we build directly impact how quickly and safely we can improve merchant outcomes.

The Role

You will own the operational lifecycle of our ML systems: deployment pipelines, evaluation frameworks, data pipelines, and the monitoring and reliability layer that keeps everything running in production. You'll ensure models go from training to production safely, that we can evaluate changes rigorously, and that the data feeding our models is fresh and correct.

This role is the connective tissue between research and production. You'll build the systems that let engineers ship model improvements with confidence and speed, while maintaining the reliability standards required to serve hundreds of millions of buyers - including during peak events like Black Friday/Cyber Monday.

This role carries real technical authority. You'll set the standards for how models get deployed and evaluated, mentor engineers on operational best practices, and drive alignment on reliability and pipeline strategy across the team. You'll influence technical direction beyond your immediate team and raise the engineering bar through hiring and technical reviews.

What You'll Do

Deployment & Rollout

• Own the model deployment pipeline end to end: export, validation, canary rollout, rollback, and A/B integration
• Build and maintain CI/CD for ML: automated testing, model validation gates, and progressive delivery
• Ensure safe, repeatable deployments with clear rollback paths and minimal manual intervention

Evaluation & Experimentation

• Build automated offline evaluation pipelines against production baselines
• Extend our experimentation framework so ML Engineers can launch and evaluate model changes with minimal friction
• Maintain evaluation datasets and ensure data freshness and correctness
• Integrate offline metrics with online A/B testing to close the feedback loop

Data Pipelines

• Own data preprocessing for training: interaction histories, feature stores, and embedding tables
• Manage workflow orchestration (Airflow or equivalent) for scheduled retraining and data refresh. You trigger and monitor training runs; the underlying GPU compute layer is owned by the infrastructure side of the team.
• Ensure data quality, lineage tracking, and pipeline idempotency
• Own data correctness and freshness; partner with infrastructure engineers on data loading throughput and efficiency

Monitoring & Reliability

• Build monitoring and alerting across training jobs, serving endpoints, and data pipelines
• Define and maintain SLOs for model freshness, serving latency, and training throughput
• Participate in incident response and drive post-mortems for ML system failures
• Identify and eliminate toil through automation

Technical Leadership

• Drive cross-team technical strategy for ML operations - identify systemic reliability risks and pipeline bottlenecks before they become incidents
• Mentor and up-level engineers on the team through pairing, design reviews, and setting operational standards
• Contribute to hiring: screen candidates, conduct technical interviews, and calibrate the engineering bar
• Write technical proposals and RFCs that shape operational direction across the organization

What We're Looking For

Required

• 7+ years in software engineering, with 5+ years focused on MLOps, data engineering, or production ML systems
• Strong experience with ML deployment pipelines: model export, validation, canary releases, and rollback strategies
• Experience with workflow orchestration for ML (Airflow, Dagster, Prefect, or similar)
• Solid Python fundamentals; comfortable working with PyTorch model artifacts and training configurations
• Production monitoring experience: you've built or operated alerting, dashboards, and SLO frameworks for ML systems
• Experience with data pipelines at scale: batch processing, feature engineering, and data quality validation
• Working proficiency with Kubernetes: able to debug pod failures, understand resource scheduling, and navigate GPU workloads
• Demonstrated technical leadership: you've driven operational strategy, written technical proposals, and influenced engineering direction beyond your immediate team
• Track record of mentoring engineers and raising the reliability bar on a team

Preferred

• Experience with large-scale data warehouses (BigQuery or equivalent) for offline evaluation and metrics
• Hands-on with experiment tracking and A/B testing frameworks
• Experience operating recommendation or retrieval systems at scale
• Familiarity with model compression workflows in production (quantization, pruning, distillation)
• Experience with cloud-native ML orchestration (SkyPilot, Ray, or similar)

How We Work

• You'll pair directly with ML Engineers. Understanding their models well enough to build the right operational workflows is part of the job.
• We prefer automation over runbooks. If a process can be scripted, it should be.
• On-call is shared. When you're on rotation, your scope is pipeline failures, data freshness alerts, deployment rollbacks, and evaluation integrity - you own it end to end.
• You'll dig into Airflow DAG failures, data drift alerts, and deployment validation issues. This is a deeply operational role with high production stakes.
• Research and production are the same codebase. You'll see your operational decisions reflected in real model quality and real merchant outcomes.
• Shopify operates on high trust and low process. You'll have real ownership and the autonomy to make decisions, not just execute tickets.

What Success Looks Like

• In 3 months: You've onboarded to deployment and evaluation pipelines, shipped at least one meaningful improvement to deployment safety or developer experience, and can independently debug issues across the operational stack.
• In 6 months: You own a major subsystem (deployment pipeline, evaluation framework, or data pipelines). Researchers are shipping model changes faster or more safely because of improvements you've made.
• In 12 months: You've shaped the operational roadmap for ML systems and influenced engineering direction beyond the team. Deployments are faster and safer, evaluation is more rigorous, and the team trusts the pipelines you've built. Other engineers across the organization come to you for guidance on ML operational best practices. You've made the team stronger through hiring and mentorship.

We're building the future of Real-Time Merchant Analytics at Shopify!

As a Staff Engineer you'll be at the forefront of reimagining how merchant data flows through modern streaming architectures. This isn't your typical infrastructure role – you'll be crafting solutions that challenge conventional approaches to data processing at global scale.

What Makes This Exciting?

You'll work across multiple languages and technologies – Java, Ruby, Python, SQL, Flink, and ClickHouse – choosing the right tool for each challenge, model data elegantly, and turning data pipeline development into a configuration exercise rather than a coding marathon.

You'll tackle fascinating problems: How do you architect lightning-fast real-time modeling that seamlessly combines data from multiple tables? How do you handle late-arriving data in distributed streams? What's the most elegant approach to backfill terabytes while maintaining real-time processing?

We embrace AI and LLMs to accelerate repetitive tasks, freeing you to focus on the creative problem-solving that makes this work truly rewarding.

If you love turning "impossible" requirements into beautiful solutions, this is your playground.

What You'll Do

• Architect, build, and refine high-performance streaming infrastructure tailored to large-scale, real-time merchant analytics.
• Develop tools and frameworks to boost platform efficiency, scalability, and developer experience across the team.
• Collaborate with cross-functional teams to integrate streaming systems with Shopify's broader data ecosystem.
• Partner with product and data teams to influence the technical roadmap and shape the future of merchant analytics.
• Mentor and uplevel engineers on the team, fostering an environment of innovation and technical excellence.

What You'll Need

• Extensive experience in data infrastructure engineering, particularly in building and scaling real-time data platforms.
• Strong knowledge of Apache Flink or similar stream processing frameworks (Kafka Streams, Spark Streaming).
• Proficiency in multiple programming languages (Java, SQL required; Python, Ruby a plus).
• Experience with analytical databases like ClickHouse or BigQuery.
• Strong understanding of containerization (Docker, Kubernetes).
• Deep expertise in handling distributed systems challenges: late-arriving data, exactly-once semantics, backfill strategies, and data consistency.
• Outstanding problem-solving skills with a focus on complex technical challenges at scale.
• A collaborative mindset and the ability to thrive in a diverse, dynamic team environment.

About the role

Join Shopify's dynamic engineering team, where code is core and innovation drives commerce forward. As a Senior Staff Engineer on the Measurement team, you'll lead the development of cutting-edge marketing analytics tools that empower merchants to optimize customer acquisition and maximize return on ad spend. Collaborate with data engineers, product teams, and partners to build unbiased insights, integrations, and reports that analyze paid advertising performance. Help shape the future of e-commerce analytics in a fast-paced environment, solving complex attribution challenges to make data-driven decisions accessible for entrepreneurs worldwide.

Key Responsibilities:

• Design scalable data pipelines for marketing analytics and attribution models measuring ad impact.
• Build integrations with partners (e.g., Google, Meta, Pinterest, TikTok) for real-time ad data processing.
• Develop analytics systems using streaming tech like Flink and Kafka for large datasets and timely insights.
• Own end-to-end delivery of measurement tools, from prototyping to production APIs and reports.
• Optimize data processing efficiency with SQL, ClickHouse, and analytics for fast query latencies.
• Influence product roadmap and advocate strategic direction with executive stakeholders.
• Evolve measurement infrastructure for emerging paradigms like multi-touch attribution and AI metrics.
• Mentor engineers and collaborate on unbiased insights to optimize merchant ad strategies.

Qualifications:

• Proven data engineering experience building scalable analytics systems.
• Strong skills in streaming tech like Apache Flink, Spark, and Kafka for real-time processing.
• Expertise in analytics, attribution modeling, and marketing/e-commerce metrics.
• Experience with APIs for data integrations and strong SQL proficiency.
• Ruby is a plus but not required.
• Skills in performance optimization for data pipelines and large datasets.
• Strong collaboration with teams like data scientists and product managers.

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 pair programming interview, using your own IDE.

This role may require on-call work.

Ready to craft the world’s best marketing analytics tools and drive data-powered commerce forward? Join us and make commerce better for everyone.

Step into the engine room of Agentic Commerce! Imagine owning the bleeding edge of machine learning at Shopify, where your acceleration, optimization, and scaling of ML inference will shape the experience of millions of merchants, and influence how commerce AI is done worldwide. We’re seeking a Senior Staff Engineer to architect, optimize, and own the high-performance runtime that transforms innovative models into production breakthroughs. Your work will be the engine behind our real-time AI systems, driving game-changing cost and latency reductions, and enabling rapid launches of intelligent features that keep Shopify (and our merchants) years ahead. Join a remote-first team of world-class experts, experiment fearlessly, and see your code move the needle for some of the largest-scale ML workloads in commerce.

Responsibilities

• Architect, optimize, and own Shopify’s production ML inference. Designing for high throughput, ultra-low latency, and global reliability.
• Leverage and extend technologies like CUDA, TensorRT, Triton, TVM, and custom GPU kernels to deliver state-of-the-art performance and efficiency at scale.
• Partner with ML, infrastructure, and product teams to seamlessly deploy, benchmark, and scale cutting-edge models powering our platform.
• Drive cost optimization and system efficiency, reducing cloud spend and carbon footprint by orders of magnitude without sacrificing model quality.
• Lead deep performance investigations, apply advanced techniques (pruning, quantization, distillation, batching), and implement robust solutions for serving models in production.
• Set technical strategy and culture for ML inference across Shopify, mentoring others and collaborating with global AI pioneers.

Qualifications

• Proven, hands-on expertise in building and optimizing large-scale ML inference systems, with measurable performance and cost wins.
• Deep experience in production model serving, runtime optimization, and acceleration. Especially leveraging GPUs (CUDA, TensorRT) and high-performance deep learning infrastructure.
• Strong software engineering skills (Python, C++, and/or other relevant languages) with a robust systems and distributed computing mindset.
• Demonstrated leadership in architecting or scaling reliable, real-time inference at scale, handling millions of queries per day.
• Track record of cross-functional impact: working closely with ML research/engineering, infra, and product teams to deliver production results.
• Advanced understanding of model compression, quantization, efficient deployment, and tradeoffs between speed, cost, and accuracy.

Nice to Haves

• Open source contributions to inference frameworks (TensorRT, TVM, Triton, DeepSpeed, ONNX, etc.) or technical talks/publications at leading AI conferences.
• Experience optimizing inference across a variety of hardware (NVIDIA, AMD, ARM, cloud TPUs).
• Familiarity with building or integrating robust monitoring, observability, and auto-scaling for inference platforms.
• Experience with modern MLOps pipelines and methodologies.
• Prior experience in e-commerce, large-scale product infra, or globally distributed inference workloads.

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.

Back

Senior Software Engineer- Streaming Pipelines

• Remote - Americas
• Engineering & Data

Apply Now

About the role

Looking for a Senior Software Streaming Pipelines Engineer to join our team reimagining how merchant data flows through modern streaming architectures. This isn’t your typical ETL role – you’ll be crafting solutions that challenge conventional approaches to data processing at global scale.

What makes this exciting?

• We’ve developed a declarative pipeline framework using Apache Beam, Google Cloud Dataflow, and ClickHouse that transforms how data engineers build streaming systems. Imagine YAML-driven pipelines that eliminate boilerplate code, real-time stream processing across multiple regions, and an architecture that makes complex data transformations feel effortless.
• You’ll work across multiple languages – Kotlin, Ruby, Python, and Rust – choosing the right tool for each challenge, alongside dbt for elegant data modelling and our custom framework that turns pipeline development into a configuration exercise rather than a coding marathon.
• The puzzle? Replacing entrenched batch systems with streaming-first architecture while merchants never notice the transition.
• You’ll tackle fascinating problems: How do you handle late-arriving data in distributed streams? What’s the most elegant approach to backfill terabytes while maintaining real-time processing? How do you architect lightning-fast real-time modelling that seamlessly combines data from multiple tables?
• We embrace AI and LLMs to accelerate repetitive tasks, freeing you to focus on the creative problem-solving that makes this work truly rewarding.

If you love turning “impossible” requirements into beautiful solutions, this is your playground.

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.

Apply Now

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At Shopify, we're making commerce better for everyone, from independent entrepreneurs to global brands. We provide the scalability, reliability, and flexibility merchants need to build, grow, and evolve their businesses. Shopify powers commerce for brands like AllBirds, Gymshark, Staples, and more.

Our Professional Services Engineering team partners directly with these brands through paid engagements to build custom integrations, accelerate product features, and deliver partner solutions that standard approaches can't handle. We bridge the gap between enterprise merchant needs and what Shopify offers out of the box – building apps that connect Shopify systems with merchant systems, payment integrations, sales channels, and custom platform extensions.

This is where Senior Engineers come in, working closely with Plus and enterprise merchants, partners, Delivery Managers, Solution Architects, and Product teams to solve complex challenges. You'll be the primary technical point of contact on your projects, leading both the strategy and hands-on implementation.

What you'll do

• Partner with Plus and enterprise merchants to architect and deliver custom integrations through paid engagements
• Lead technical discovery, solution design, and hands-on implementation for backend systems
• Build apps that connect Shopify with merchant systems (ERPs, fulfillment, inventory, payments, sales channels)
• Collaborate with Product and Engineering teams across Shopify to accelerate backlog features and meet merchant launch timelines
• Develop payment integrations, sales channels, and custom platform extensions for partners
• Investigate and resolve production issues for apps your team owns
• Communicate directly with merchants and partners – set expectations, surface risks early, and land clear recommendations
• Write well-tested code using test-driven development or similar practices
• Share learnings from projects with the team to raise the bar for everyone
• Leverage AI tools (Cursor, Claude, π) to accelerate development and ramp up on unfamiliar codebases

What you can expect

• A hands-on technical role collaborating with high-impact collaborations with merchants and partners
• A fast-paced environment where requirements change frequently and ambiguity is the norm
• Autonomy to solve complex problems creatively, plus mentorship and opportunities to grow from each challenge
• Direct influence on merchant success and Shopify's product roadmap
• Projects typically lasting six months or less – you'll gain broad expertise across many Shopify domains rather than deep specialization in a single area
• A culture that values authenticity, openness, and diverse perspectives

Your skills and experience

We're interested in what you can do. If you're excited about the role, please apply even if your experience doesn't line up perfectly.

Experience can come from formal education, self-directed learning, or previous roles. No specific degree required (skills first).

Technical expertise

• Ruby on Rails: Strong backend development experience, including background jobs, webhooks, and API design
• React: Frontend experience for building admin interfaces and merchant-facing UIs
• Node.js: Familiarity with JavaScript/TypeScript backend services
• GraphQL and REST APIs: Proficiency with Shopify's Admin and Storefront APIs, webhooks, and rate-limit-aware patterns
• Relational databases: Experience with SQL, data modeling, and query optimization
• System integration patterns: Building reliable connections between Shopify and external systems (ERPs, fulfillment, payments)

Development workflow and tooling

• Git/GitHub flows, environments, CI/CD pipelines
• Test-driven development practices
• Proven use of AI tools (Cursor, VS Code/Copilot, Claude, π) to ship production work
• Comfort utilizing AI tools to ramp up on unfamiliar codebases and accelerate development

Communication and collaboration

• Strong communication skills with both technical and non-technical stakeholders
• Ability to translate ambiguous merchant requirements into clear technical solutions
• Comfortable as the primary technical contact for merchants and partners
• Clear written and verbal communication for client-facing engagements
• Thrives in collaborative environments with cross-functional teams (Engagement Managers, Delivery Managers, Solution Architects)

Bonus if you have

• Experience with Shopify app development (public or custom apps), especially react-router
• Familiarity with payment processing, payment app development, or PCI compliance
• Experience with ERP/OMS/PIM integrations
• Background in consultancy or agency work with enterprise merchants
• Experience building internal tools and automation
• Knowledge of Shopify Plus features (B2B, Scripts/Functions, Launchpad)

How you'll work

You'll work on projects typically lasting six months or less, from hands-on delivery to technical leadership on the engagement. You'll work in a dynamic environment where priorities shift frequently, requirements evolve, and creative problem-solving is essential.

This role requires both deep technical execution and the ability to think strategically about merchant outcomes. You'll be expected to learn new frameworks and codebases quickly – and AI tools are your accelerator, not a crutch.

*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 pair programming interview, using your own IDE.

This role may require on-call work. Ready to craft your next masterpiece in code? Join the team that’s making commerce better for everyone.*

The role's core focus is on building and managing Shopify's compliance programs for our advanced IT systems, it offers a unique opportunity to work in a flexible compliance environment where expertise, innovation, and unconventional approaches are highly valued.

In this role, you have the autonomy to discover, analyse, and solve security and compliance problems at scale. Resourcefulness is key - you’ll need to be able to quickly gather context on infrastructure, systems, software, and safeguards to help Shopify continue shipping and scaling while staying secure, trustworthy, and usable.

A ‘day in the life’ of this role may include any, or all, of the following:

• Writing and updating code that automates and supports audit and compliance programs.
• Meeting with SMEs from Production Engineering, Security Engineering, Product, Legal, and other areas to learn how Shopify works and ensure that the compliance programs accurately reflect what we do and how we do it.
• Engaging with external auditors to design and perform audits for programs such as SOC, SOX, PCI and others.
• Providing expert advice to Shopify teams with regard to security and compliance domains you manage

We want a dynamic technical expert capable of managing projects, solving complex problems, simplifying solutions, and inspiring and up-skilling the team.

This role is ideal for you if you are someone who enjoys being hands-on and building technical things to support your work. You must also be able to organize others as you build and manage complex security compliance programs for a fast paced engineering focused environment.

Qualifications:

• Proven experience performing assurance and advisory roles relating to Information Technology with particular emphasis on system implementations, technical security configurations, and cloud native environments
• Hands-on experience building data analytics, reporting solutions and task automation tooling
• Experience evaluating IT, security and application controls in the context of a compliance program for a company of similar size and complexity of Shopify
• Strong knowledge of industry risk and compliance frameworks such as NIST, ISO, SOX, SOC, and PCI-DSS
• Excellent analytical and problem-solving skills, with the ability to think strategically and identify innovative solutions to complex challenges
• Strong project management skills, with the ability to prioritize and manage multiple initiatives simultaneously using agile project management methodologies
• Exceptional communication and interpersonal skills, with the ability to effectively collaborate with stakeholders
• Self-motivated and adaptable, with a strong drive for continuous learning and professional growth

Responsibilities:

• Be a security expert responsible for owning and building compliance activities for standards such as: SOC, PCI, SOX and others
• Dive deep into new products or initiatives to surface and analyse the impact on security compliance engineering
• Leverage data and visualization tools to identify areas for improvement, track progress and inform trusted decisions
• Be a strong and credible influencer among cross functional engineering and business teams
• Actively seek out opportunities to develop and deploy automations that will increase team efficiency
• Anticipate changes in our trust and security posture as the technical footprint and company operations change, and help propose solutions to adapt to change
• Develop safeguards, systems and policies that meet compliance requirements while balancing the need to move fast and stay innovative

Step into the intersection of engineering and data science at Shopify as a Search Relevance Engineer. You'll be designing and implementing AI-powered search and discovery solutions that empower our merchants and revolutionize their experiences. In a role that thrives on change and mastery, you'll push the boundaries of what's possible, crafting tangible solutions that make a real difference in the daily lives of entrepreneurs.

Key Responsibilities:

• Collaborate with data scientists and engineers to productionize data products through load testing, metrics analysis, and experimentation.
• Design and implement features to enhance search and recommendation relevance, including semantic search, query understanding, and personalization.
• Build and maintain data pipelines for information retrieval systems.
• Tune queries that power search and discovery experiences.
• Develop tools for evaluation and relevance engineering, adhering to high-quality software engineering practices.
• Prioritize and communicate effectively with both technical and non-technical audiences.
• Mentor engineers and data scientists.

Qualifications:

• Mastery of relevance engineering, with experience in discovery and recommendation systems powered by Elasticsearch, Solr, Lucene, or vector databases.
• Extensive experience using Python, with a strong grasp of object-oriented programming (OOP) fundamentals.
• Ability to write efficient, optimized code with low latency requirements.
• Strong software development background, with proven problem-solving skills and technical system thinking.
• Experience in training, evaluating, testing, and deploying machine learning, natural language processing, or generative AI products at scale is a plus.
• Familiarity with statistical methods like regression, GLMs, or experiment design and analysis is welcome.
• Exposure to other languages such as Ruby, Rails, Rust, or Typescript is advantageous.
• This role may require on-call work.

*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 pair programming interview, using your own IDE.

This role may require on-call work. Ready to join a team of driven crafters building world-class search and discovery experiences? Join us and make commerce better for everyone.*

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