Lead Machine Learning Engineer

What you'll do

• Build and optimize distributed training pipelines for petabyte-scale data across GPU clusters.
• Develop and refine neural-network architectures, loss functions, and training strategies for autonomy tasks.
• Create scalable data-processing workflows and tools for experimentation, monitoring, and model iteration.

What they ask for

• Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
• At least 5 years of professional experience developing, training, and deploying machine-learning models in production environments.
• Hands-on experience training models across multiple GPUs or compute nodes, with distributed-training frameworks and large datasets.
• Strong Python programming skills and solid knowledge of neural networks, optimization, loss functions, model evaluation, and training methodologies.

Nice to have

• Experience with robotics or autonomous-driving datasets and multimodal sensor inputs.
• Experience identifying training bottlenecks; multimodal model development; or peer-reviewed publications or significant research contributions.

Pay

$177k - $215k CAD base salary; $225K – $260K (base salary not specified)

Tools

Python, GPU

AI focus

Machine learning for robotics autonomy, including multimodal model training and distributed training systems.