Senior ML Scientist - From Scratch to Production in Vision

BCtechjobs; Vancouver; On-site; CAD 100,000 - 150,000; Full time

Job summary

BCtechjobs is partnering with a Canadian-founded tech company to hire a mid-to-senior Machine Learning Scientist in the Greater Vancouver area. You will join a team building cutting-edge computer vision models for industrial applications and see your work shipped at scale.

You will own the full arc of model development—from design on the whiteboard to prototype, training, evaluation, and deployment—using PyTorch (and JAX) on massive datasets, with collaborators across data engineering and

Qualifications

• Master's or PhD in Computer Science, Electrical Engineering, or a related discipline.
• 3+ years of hands-on experience developing and researching deep learning models, with a focus on building architectures from scratch.
• Proficiency in Python and PyTorch (with familiarity in JAX).
• Experience with vision tasks such as object detection, instance/semantic segmentation, or anomaly detection.
• Excellent communication skills to translate model behavior to non-ML audiences and collaborate with domain experts.

Responsibilities

• Own the full arc of model development from research to deployment.
• Design and implement deep learning architectures for large industrial datasets.
• Collaborate with data engineers and platform teams to productionize models.

Skills

Python

Deep learning

Computer vision

Model development

Research experience

Education

Master's or PhD in Computer Science or Electrical Engineering

Tools

PyTorch

JAX

Job description

BCtechjobs is partnering with a Canadian-founded tech company to hire a mid-to-senior Machine Learning Scientist in the Greater Vancouver area. You will join a team building cutting-edge computer vision models for industrial applications and see your work shipped at scale.

You will own the full arc of model development—from design on the whiteboard to prototype, training, evaluation, and deployment—using PyTorch (and JAX) on massive datasets, with collaborators across data engineering and