Machine Learning jobs in Canada

100% Remote

AI/ML Engineer – Large Language Model Pretraining (100B+ Parameters)

Gen AI Engineer creating and developing LLMs from "Test to Production".

West Coast 100% Remote

As a Gen AI Engineer, you will lead the pre-training of massive LLMs (100B+ parameters), requiring deep expertise in distributed training, large‑scale optimization, and model architecture. This is a rare opportunity to work with petabyte-scale datasets and cutting‑edge compute clusters in a high‑impact environment.

• Architect and implement large‑scale training pipelines for LLMs with 100B+ parameters.
• Optimize distributed training performance across thousands of GPUs/TPUs.
• Collaborate with research scientists to translate experimental results into production‑grade training runs.
• Manage and preprocess petabyte-scale datasets for pretraining.
• Implement state‑of‑the‑art techniques in scaling laws, model parallelism, and memory optimization.
• Conduct rigorous benchmarking, profiling, and performance tuning.
• Contribute to client research in LLM architecture, training stability, and efficiency.

• Advanced degree (PhD or Master’s) in Computer Science, Machine Learning, or related field from a top‑20 global university in CS.
• 3+ years of hands‑on experience with large‑scale deep learning model training.
• Proven experience in pretraining models exceeding 10B parameters, preferably 100B+.
• Deep expertise in distributed training frameworks (DeepSpeed, Megatron‑LM, PyTorch FSDP, TensorFlow Mesh, JAX/TPU).
• Proficiency with parallelism strategies (data, tensor, pipeline) and mixed precision training.
• Experience with large‑scale cloud or HPC environments (AWS, Azure, GCP, Slurm, Kubernetes, Ray).
• Strong skills in Python, CUDA, and performance optimization.
• Strong publication record in top‑tier ML/AI venues (NeurIPS, ICML, ICLR, ACL, etc.) preferred.

• Experience with LLM fine‑tuning (RLHF, LoRA, PEFT).
• Familiarity with tokenizer development and multilingual pretraining.
• Knowledge of scaling laws and model evaluation frameworks for massive LLMs.
• Hands‑on work with petabyte‑scale distributed storage systems.

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