Senior Machine Learning Systems Engineer to optimize large language model (LLM) inference performance across AWS Trainium platforms, including Trainium2 and Trainium3.

The ideal candidate will have solid hands-on experience in Python, PyTorch, LLM inference frameworks, and accelerator performance optimization. This role focuses on improving inference latency, throughput, scalability, and cost efficiency for production-grade AI workloads.

Optimize LLM inference performance, focusing on decode latency, throughput, and serving costs.

Work with AWS Trainium2/Trainium3, AWS Neuron SDK, and vLLM on Neuron.

Optimize prefill/decode performance, continuous batching, KV-cache, and tensor/expert parallelism.

Work with advanced AI architectures, including Mixture of Experts (MoE) and State Space Models.

Develop benchmarking reports, technical documentation, implementation guides, and customer runbooks.





Collaborate with customer engineering teams to deliver measurable performance improvements.

5+ years of experience in Machine Learning or Systems Engineering.

2+ years of hands-on experience with production LLM inference or serving.

Strong programming experience in Python and PyTorch.

Hands-on experience with LLM serving frameworks such as vLLM, TensorRT-LLM, SGLang, or Triton Inference Server.

Experience with performance profiling, benchmarking, and accelerator workload optimization.

Strong analytical, problem-solving, and technical communication skills.

Experience with AWS Trainium, Inferentia, AWS Neuron SDK, NxD Inference, or Neuron Kernel Interface (NKI).

Previous experience in customer-facing engineering, consulting, or technical enablement. #

📌 Senior Machine Learning Systems Engineer (Winnipeg)
🏢 Aptino
📍 Winnipeg