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ML Engineer
01
GPU Architecture Comprehension
Mentally model hierarchical compute structures06
Distributed Training Topology Design
Map data and model parallelism strategies to cluster layouts07
Inference Latency Profiling
Identify bottleneck layers through timing analysis08
Quantization Trade-off Analysis
Evaluate precision reduction effects on model accuracy09
Dynamic Batching Feasibility Assessment
Model latency variance under variable batch sizes10
Transformer Attention Bottleneck Resolution
Diagnose quadratic complexity scaling in sequence length11
Production MLOps Orchestration
Connect inference serving patterns to GPU resource allocation12
NVLink Topology Exploitation
Map collective communication primitives to physical interconnect13
Sparse Model Acceleration Strategy
Evaluate structured vs unstructured sparsity impact on kernels14
Custom Kernel Fusion Justification
Reason about memory traffic reduction through operator fusion15
Multi-GPU Scaling Bottleneck Diagnosis
Model strong-scaling efficiency loss from communication overhead16
LLM Token Throughput Maximization
Optimize prefill and decode phase execution separately17
Compilation and Auto-tuning Trade-offs
Weigh JIT compilation latency against runtime optimization gains18
NVIDIA Software Stack Integration
Connect cuDNN, cuBLAS, and NCCL selection to workload requirements19
System-wide Performance Prediction
Construct end-to-end latency models from component bottlenecks20