Machine Learning Engineer (Canada) Full Time

Jobright is a next-generation AI job search platform built to make career navigation faster, smarter, and more personal. They are looking for a Machine Learning Engineer to design, train, and ship the models that power how our AI agents understand resumes, match jobs, and guide people through their careers.

Why Join Us

• Build models that directly shape the career outcomes of millions of job seekers, not abstract benchmarks on a leaderboard
• Take ownership of the full ML lifecycle, from data and training to deployment, monitoring, and iteration
• Work alongside engineers and researchers who care about model quality, system design, and shipping speed in equal measure
• Join a team that invests in serious ML infrastructure, so you can spend your time on modeling instead of glue code

Responsibilities

• Design, train, and deploy machine learning models that power core capabilities across the Jobright platform, including matching, ranking, and recommendation
• Build and maintain training pipelines, feature stores, and evaluation frameworks that make experimentation fast and results trustworthy
• Partner with product, engineering, and applied AI teammates to translate ambiguous product goals into well-defined modeling problems and shipped features
• Monitor model performance in production, diagnose regressions, and feed those learnings back into the next iteration

Qualifications

Required

• Early-career engineer with 1 to 3 years of experience in machine learning, data science, or a closely related technical field
• Clear communicator who can explain modeling choices, tradeoffs, and results to both technical teammates and non-technical stakeholders
• Solid grounding in classical ML and modern deep learning, including the fundamentals of training, evaluation, and deploying models at scale

Preferred

• Prior internship or industry experience in ML engineering, applied research, or data science at a tech or AI-focused organization
• Track record of shipping production ML systems in fast-moving environments where data, requirements, and priorities shift regularly
• Hands-on skills in Python, PyTorch or TensorFlow, distributed training and inference, MLOps tooling, and SQL-based data systems