Machine Learning jobs in Canada
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Outlier
Senior Gen AI Engineer
Job summary
A leading technology firm is seeking an experienced AI/ML Engineer to lead the pre-training of massive LLMs (100B+ parameters) in a fully remote setting. You will be responsible for architecting training pipelines, optimizing distributed training, and managing extensive datasets. The ideal candidate holds an advanced degree and has a proven track record in large-scale model training and distributed frameworks. Join us to work with cutting-edge technology in a high-impact environment that shapes the future of AI.
Qualifications
- Advanced degree from a top-20 global university in CS.
- 3+ years hands-on experience with large-scale model training.
- Proven experience in pretraining models exceeding 10B parameters.
- Deep expertise in distributed training frameworks.
- Experience with large-scale cloud environments.
Responsibilities
- Architect training pipelines for LLMs with 100B+ parameters.
- Optimize distributed training performance.
- Collaborate with research scientists to implement training runs.
- Manage and preprocess petabyte-scale datasets.
- Conduct benchmarking and performance tuning.
Skills
Education
Tools
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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* The salary benchmark is based on the target salaries of market leaders in their relevant sectors. It is intended to serve as a guide to help Premium Members assess open positions and to help in salary negotiations. The salary benchmark is not provided directly by the company, which could be significantly higher or lower.