Some companies are “AI-first.” Others are just very good at adding “AI” to slide decks. This role sits firmly in the first category. A fast-moving technology organization is hiring a Platform Engineer to build the infrastructure layer behind large-scale AI-enabled services used across internal and customer-facing systems. You’ll own core compute and orchestration infrastructure, scalable model-serving environments, and the increasingly delicate task of balancing performance, reliability, and infrastructure efficiency at scale. This is equal parts systems engineering, distributed infrastructure, and platform architecture. You’ll work across Kubernetes, workload orchestration, lifecycle management, and intelligent request routing while helping engineering teams deploy and scale advanced services without rebuilding infrastructure from scratch every quarter. Expect deep technical ownership, meaningful architectural influence, and occasional reminders that “just add more hardware” is not actually a scaling strategy. The environment is builder-heavy, technically sharp, and optimized for engineers who prefer solving difficult infrastructure problems over attending meetings about difficult infrastructure problems. Required Skills & Experience Deep expertise with cloud-native infrastructure across AWS and/or GCP, Kubernetes, and infrastructure-as-code frameworks such as Terraform, Pulumi, or similar tooling Strong hands-on experience supporting accelerator-backed workloads, large-scale model-serving environments, or high-throughput inference infrastructure in production Advanced software engineering experience in Python, Go, or C++, including building scalable internal platforms, APIs, automation tooling, and distributed systems Desired Skills & Experience Experience improving performance, scalability, and resource efficiency across distributed compute or inference environments Familiarity with distributed serving architectures, multi-workload compute environments, or accelerator-backed infrastructure supporting a variety of application types Exposure to model optimization techniques, lightweight adaptation workflows, edge deployment concepts, or infrastructure-level scheduling and performance tuning Daily Responsibilities Hands-On Engineering: 70% Team Collaboration & Cross-Functional Work: 30% Benefits Medical, Dental, and Vision Insurance Vacation Time Use of AI in Hiring: No Applicants must be currently authorized to work in Canada on a full-time basis now and in the future. Accommodation will be provided in all parts of the hiring process as required under Motion Recruitment’s Employment Accommodation policy. Applicants need to make their needs known in advance. #J-18808-Ljbffr