Machine Learning Engineer

ML Ops Engineer

Salary: CA$90k - CA$160k, plus competitive package

Location: Remote (Canada-based, Toronto preferred)

Are you a highly skilled Machine Learning Operations Engineer looking for a role where your work directly impacts real-world outcomes and redefines how a traditional sector operates? I'm partnering with an innovative, well-funded AI company that's truly disrupting its industry, and they're looking for someone exceptional to join their Platform Team.

This is a remote-first opportunity, but with a strong preference for candidates in the Toronto area to facilitate occasional in-person collaboration.

Why This Role is Different:

My client's core product uses sophisticated ML models to generate 3D predictive maps and derive critical insights from unique datasets. Your work will directly influence client operations and financials, significantly boosting efficiency. They're backed by top investors like Founders Fund, Y Combinator, and even OpenAI's former Chief Scientist, joining a passionate team of 15 pushing industry boundaries.

What You'll Be Doing:

As their newest Machine Learning Operations Engineer, you'll be the backbone of their AI operations. Your responsibilities will include:

• Develop & Maintain ML Pipeline Infrastructure: Design data processing, reproducible training, model registries, and automated validation.
• Optimize Model Deployment: Containerise models, implement serving infrastructure, and create performance monitoring and alerting.
• Build & Enhance Internal Tooling: Create specialized tools for data processing, 3D vizualisation, and performance metrics.
• Drive Engineering Excellence: Refactor code, implement quality standards, optimize systems, and contribute to documentation.

What We're Looking For:

We're seeking a passionate engineer who thrives in a fast-paced, innovative environment.

Must-Haves:

• 3+ years of strong software engineering experience with a focus on Python development.
• Proven experience with ML infrastructure and deployment pipelines.
• Proficiency with containerization technologies (Docker).
• Strong command of data processing libraries (NumPy, pandas).
• Familiarity with cloud environments (AWS).
• Experience with CI/CD practices and tools.
• A track record of writing clean, well-tested, and maintainable code.
• Deep understanding of the ML lifecycle and experience with model registry/versioning systems.
• Background in implementing monitoring solutions for ML systems.
• Knowledge of database systems and SQL, plus familiarity with experiment tracking.

Nice-to-Haves:

• Experience with deep learning frameworks (PyTorch).
• Familiarity with Kubernetes or other orchestration technologies.
• Knowledge of infrastructure-as-code principles.
• Experience optimizing data pipelines for performance.
• Understanding of 3D data processing and visualization.

Educational Background:

• A Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent practical experience.

Ready to make a tangible impact with cutting-edge AI in a high-growth company? Let's connect!