Optimize large language model inference with Python and PyTorch across AWS Trainium platforms, including Trainium2 and Trainium3. Improve production AI performance through hands-on accelerator optimization.
As a Senior Machine Learning Systems Engineer, you will improve inference latency, throughput, scalability, and serving costs. The work includes LLM inference frameworks, AWS Neuron SDK, vLLM on Neuron, and production workload optimization. You will evaluate advanced techniques for Qwen-family models.
Key Responsibilities: Optimize decode latency, throughput, and LLM serving costs
Work with Trainium2, Trainium3, Neuron SDK, and vLLM
Evaluate speculative decoding and Multi-Token Prediction for Qwen models
Profile and benchmark GPU and Trainium production workloads




Optimize prefill, decode, batching, KV-cache, and parallelism
Requirements: Five-plus years in Machine Learning or Systems Engineering
Two-plus years with production LLM inference or serving
Solid Python and PyTorch programming experience
Experience with vLLM, TensorRT-LLM, SGLang, or Triton
Understanding of prefill, decode, batching, and KV-cache management
Apply inference performance engineering, Python, PyTorch, and accelerator optimization across demanding production LLM workloads.

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📌 Senior LLM Inference Performance Engineer (Manitoba)
🏢 Aptino
📍 Manitoba