StudyFetch is the #1 AI-native learning platform globally, transforming how millions of students learn through personalized AI-powered education. We’re growing fast with backing from top-tier investors and a mission that’s redefining the future of education and ethical learning. We’re a technology company building AI-native learning products used by more than seven million students worldwide, alongside Honen, our workforce-learning platform for organizations.

Both run on the Learn Engine, the intelligence that moves a learner from initial understanding to demonstrated mastery. We work with partners like NVIDIA to bring responsible, learning-first AI to the students who need it most. All of that runs on a mature, infrastructure-as-code platform: cloud, Kubernetes, networking, deploy tooling, databases, and the GPUs that power our AI.

You’ll be the person the rest of engineering depends on to ship, and you’ll set the patterns for how we deploy, secure, and operate. You’ll also lead the next chapter: we’re building out our own GPU infrastructure inside a colocation facility, which means real networking, capacity planning, and bare‑metal platform work sitting alongside our cloud footprint.

StudyFetch started with one idea: high-quality, personalized learning should be within reach for anyone, at any stage of life. From a first job to a new career, people keep growing at every stage of life. The work is demanding and the hours can be long, and what sustains you through it is caring whether a real student finally understands the material.

Our infrastructure-as-code (Pulumi/TypeScript on GCP), the Kubernetes clusters, the Shared VPC and networking, the CI/CD and deploy tooling, secrets, and the databases behind them. You own how the whole thing fits together and how the team ships on top of it. Monitoring, alerting, incident response, and the postmortems that make the next incident less likely.

When production has a bad night,



you’re the person who understands why and makes sure it doesn’t repeat. We’re standing up GPU capacity in a colocation facility. You’ll help design and build it: hardware and capacity planning, physical and virtual networking, the platform layer that makes those GPUs usable for model serving, and the path that connects it cleanly to our cloud environment.

The GPU serving platform. The clusters and pipelines that run our self-hosted models and speech/ASR workloads, keeping latency, cost, and utilization where they need to be for real users at scale. As the work grows, you’ll shape and help grow the team that does it with you.

You’re a solid fit if either of these is true: ~7+ years building and operating production infrastructure, with real depth in cloud platform, Kubernetes, and networking, or ~ You were the founding or early infrastructure engineer who owned a significant share of a real platform. Bare-metal provisioning, datacenter or colo networking, hardware and capacity planning, GPU fleets, or standing up a hybrid of on‑prem and cloud. You use AI every day and have informed opinions about it.

You understand the demands of serving models in production, and genuine curiosity is the one thing we can’t teach. Kubernetes (GKE), Helm, container build pipelines, CI/CD (Jenkins or similar) Networking: VPC design, IPAM, firewalls, DNS/TLS, zero-trust access, and — for the colo work — physical and datacenter networking GPU & AI serving: GPU cluster operations, model/inference serving, vLLM, ASR/speech workloads, cost and latency tuning Hardware / colocation: bare-metal provisioning, capacity planning, hybrid on-prem + cloud Data: PostgreSQL and MongoDB, managed caches (Redis/Valkey), backups and disaster recovery Observability & security: monitoring and alerting, incident response, least-privilege IAM, secrets management, SOC 2-grade compliance 170,000–$270,000 base salary, plus equity ~100% employer-paid Medical, Dental, and Vision; Daily team dinner provided in-office ~

📌 Lead Infrastructure & Platform Engineer (Winnipeg)
🏢 StudyFetch Careers
📍 Winnipeg