AI Infrastructure Engineer& ML Systems Jobs
Build the compute and systems layer powering modern AI. GPU cluster engineering, inference infrastructure, model serving, and distributed training at top AI companies. $150k-$350k salaries.
AI infrastructure engineers build and operate the compute systems that make large-scale model training and inference possible. As AI models grow larger and production traffic scales, the systems layer has become one of the most critical and highest-paying specializations in the industry. These roles live at the intersection of distributed systems, GPU computing, and machine learning - requiring deep expertise in all three.
Core responsibilities include GPU cluster management and scheduling, low-latency inference serving, distributed training at scale, and storage systems optimized for large datasets and checkpoints. Engineers in this space work closely with frameworks like CUDA, Triton, and Ray, and build on cloud platforms with H100 and A100 GPU capacity. Roles span both foundational model labs (Anthropic, OpenAI, xAI) and AI product companies scaling inference for millions of users.
Related Job Categories
Frequently Asked Questions
What does an AI infrastructure engineer do?
AI infrastructure engineers design and operate the systems that run model training and serving workloads. Day-to-day work includes managing GPU clusters and scheduling (SLURM, Kubernetes), optimizing inference latency and throughput (TensorRT, vLLM, Triton), building distributed training pipelines, and operating the storage and networking infrastructure that feeds large models. At production scale, even small efficiency gains translate to significant cost and latency impact.
What skills are required for AI infrastructure roles?
Strong distributed systems fundamentals are essential - networking, storage I/O, and fault tolerance at scale. GPU programming experience (CUDA, kernel optimization) is increasingly valued as companies move beyond off-the-shelf frameworks. Kubernetes and cloud platform fluency (AWS, GCP, Azure) is expected. Most roles also require familiarity with ML frameworks (PyTorch, JAX) and inference engines (vLLM, TensorRT-LLM). Python and Go or Rust for systems tooling are common language requirements.