AI Infrastructure Engineer Jobs
Build the Foundation of AI.

AI Infrastructure Engineers build and maintain the systems that allow AI models to train and serve at scale. This includes managing GPU clusters, optimizing distributed training jobs, building ML pipelines, and ensuring cost-effective cloud resource usage. Unlike traditional DevOps, this role requires deep understanding of ML workloads, hardware acceleration, and the unique challenges of training models with billions of parameters.

Yes, DevOps and SRE experience is valuable but not sufficient. You'll need to add: 1) GPU programming basics (CUDA, memory management), 2) Understanding of ML training workflows, 3) Distributed computing for ML (data parallelism, model parallelism), 4) Cost optimization for GPU workloads. The transition typically takes 3-6 months of focused learning and building projects with actual GPU infrastructure.

Entry-level AI infra roles typically require 2-3 years of general infrastructure/DevOps experience plus demonstrated knowledge of ML systems. Mid-level roles want 4-6 years with at least 1-2 years specifically in ML infrastructure. Senior roles require 6+ years with proven track record scaling AI systems at production companies. However, strong project portfolios can accelerate this timeline significantly.

Several options: 1) Google Colab provides free GPU access for learning, 2) Lambda Labs and Vast.ai offer affordable hourly GPU rentals, 3) AWS/GCP free tiers include limited GPU access, 4) NVIDIA's Deep Learning Institute has free courses with cloud labs. Start with Colab for basics, then graduate to rented multi-GPU setups for distributed training projects.

Yes, many AI infra roles are remote-friendly since the work is primarily cloud-based. However, some companies (especially those with on-premise GPU clusters) prefer hybrid or on-site engineers. Startups and cloud-native companies tend to offer more remote flexibility. Compensation may vary by location, with SF/NY-based remote roles often paying 20-30% more than other regions.

Common paths include: 1) Senior AI Infra Engineer to Staff/Principal level ($350K-$500K+ at top companies), 2) ML Platform team lead or Engineering Manager, 3) Founding infrastructure engineer at AI startups, 4) Specialized consulting at $300-500/hour. The field is young enough that experienced AI infra engineers often move into leadership roles within 3-5 years.

It probably isn't, and you should hold the money. Most cohort dropouts are people who couldn't articulate what they were shipping when they signed up. That's why the consult exists, and why I turn down most applications. If we get on the call and you can't tell me what you'll have shipped at the end of week 8, I'll point you to the AI Native Engineer community until you can.

Yes, the cohort works for people shipping their first serious AI system whether the goal is to land a senior role or to launch a product. The shipped system serves both equally well.

You need to be able to code in Python or TypeScript. Complete beginners can follow the classroom they get access to before the cohort sessions to come in well-prepared.

You'll spend 3 hours every Tuesday in the live session and roughly 3 hours of async work in between, for 8 weeks. The Tuesday session time is fixed.

It's a four-figure investment that we discuss during the 30-minute consult, alongside whether the cohort is the right fit for your project.

Yes, most attendees do. The live session is one Tuesday a week and the async work fits around your existing schedule, as long as you can carve out roughly 6 hours a week.