ML Platform Engineer
Crush ML platform system design interviews — GPU infra, serving, and MLOps
ML platform roles are exploding, and interviews are brutal. "Design a GPU cluster scheduler," "Design a model serving platform," "Design an ML feature store" — these are real interview questions. This path covers GPU fundamentals, K8s for ML, model serving, inference cost optimization, and MLOps architecture with interview-focused system design walkthroughs.
Your Learning Path
A step-by-step roadmap from foundations to mastery. Follow this sequence for the most effective learning experience.
Modules
81 free module to get you started, plus 7 premium deep-dives.
ML Platform Engineer Roadmap
The complete landscape for ML platform engineers: GPU infrastructure, orchestration, model serving, MLOps tooling, and how this role fits between DevOps and ML engineering.
GPU Fundamentals
NVIDIA GPU architecture from an infrastructure perspective: CUDA cores, tensor cores, GPU memory hierarchy (HBM, L2, shared memory), NVLink, PCIe bandwidth, and how to read nvidia-smi output like a pro.
GPU Slicing & Multi-Tenancy
MIG (Multi-Instance GPU), time-slicing, MPS, vGPU, and fractional GPU allocation strategies. Implement fair-share scheduling for multi-tenant GPU clusters and optimize utilization rates.
Kubernetes for ML Workloads
NVIDIA GPU Operator, device plugins, topology-aware scheduling, gang scheduling, priority queues, Kueue, Volcano, and building ML training/inference clusters on Kubernetes.
Model Serving & Inference Optimization
Production model serving with vLLM, TensorRT-LLM, Triton Inference Server, and TGI. Continuous batching, PagedAttention, speculative decoding, quantization for inference (GPTQ, AWQ, GGUF), and autoscaling strategies.
Inference Cost Reduction
Strategies to cut inference costs by 50-90%: quantization, distillation, prompt caching, semantic caching, request batching, spot/preemptible instances, and building cost-aware routing layers.
MLOps & Platform Architecture
End-to-end MLOps platform design: experiment tracking, model registry, feature stores, CI/CD for ML, A/B testing infrastructure, monitoring and observability for models, and platform team organizational patterns.
Interview Prep — ML Platform Focus
System design questions specific to ML platform roles: "Design a GPU cluster scheduler", "Design a model serving platform", "Design an ML feature store." Includes sample answers, scoring rubrics, and common follow-ups.
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