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.

Not necessarily. While some programming experience is helpful, many of my clients have successfully transitioned from web development, data science, or other technical backgrounds. We'll assess your current skills during our strategy call and create a personalized plan that meets you where you are.

Most clients invest 10-15 hours per week, but this can be flexible based on your schedule. We'll have weekly 1:1 calls plus time for you to work on projects and learning. The key is consistency. Regular, focused effort beats occasional marathons.

You become a member of the AI Native Engineer community, and you stay and keep going. Career transitions take different amounts of time for everyone, and I'm not going to abandon you if things take longer. You get ongoing support through good times and bad.

Online courses give you content. 1:1 coaching gives you a personalized roadmap, direct feedback on your work, career strategy, interview prep, and accountability. You get answers to your specific questions and guidance tailored to your unique situation instead of generic advice meant for everyone.

Investment details are discussed during the 30-minute strategy call, where we'll assess your goals and create a custom plan. The program is designed to pay for itself quickly through your increased salary. Most AI engineers see a 20-50% pay increase.

Absolutely. Most of my clients work full-time and make steady progress. We'll schedule calls at times that work for you and create a realistic plan that fits your schedule. Consistency matters more than intensity.