Research and develop techniques to optimize inference for large neural networks.
Improve latency, throughput, memory efficiency, and cost per inference.
Design model-level optimizations (quantization, pruning, KV-cache).
Implement systems-level optimizations (dynamic batching, kernel fusion, multi-GPU inference).
Benchmark inference workloads across hardware accelerators.
Collaborate with engineering teams to deploy optimized inference pipelines.

🎯 Requirements

Strong background in machine learning, deep learning, or AI systems.
Hands-on experience optimizing inference for large-scale models.
Proficiency in Python and ML frameworks (e.g., PyTorch).
Experience with inference tooling (Triton, TensorRT, ONNX Runtime).
Ability to design experiments and communicate results clearly.

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