Remote anywhere in Canada, this machine learning systems engineer benchmarks and tunes production LLM serving across AWS Trainium2 and Trainium3. Python and PyTorch skills are central.

Capgemini Engineering seeks a hands-on engineer focused on accelerator performance and customer-ready inference systems. You will compare Trainium with leading GPUs, measure latency-throughput frontiers, and optimize price-performance under representative loads. The work includes Qwen-family models, vLLM, AWS Neuron, long contexts, and multi-chip serving.

Key Responsibilities:

- Benchmark workloads across Trainium generations and leading GPUs
- Control prompts, concurrency, cache state, and warm-up
- Deliver reproducible price-performance comparisons aligned with service objectives




- Apply architecture-specific optimizations to long-context workloads
- Support workshops enabling independent operation and tuning

Requirements:

- Five-plus years of machine learning or systems engineering experience
- Production inference or serving experience for two-plus years
- Hands-on AWS Trainium, Inferentia, or AWS Neuron experience preferred
- Experience profiling accelerator workloads at framework, compiler, or kernel level
- Solid written and verbal communication for technical audiences

Bring rigorous performance experimentation and accelerator migration expertise to AWS Trainium inference engineering at Capgemini Engineering.

📌 AWS Trainium LLM Systems Engineer (Toronto)
🏢 Capgemini Engineering
📍 Toronto