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

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.

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.