Staff Machine Learning Engineer, AI Generation Engine

SandboxAQ

Machine Learning Engineer focusing on end-to-end ML lifecycle at SandboxAQ. Joining AI Generation Engine to design and prototype AI-first products leveraging Large Quantitative Models.

Posted 5/27/2026full-timeRemote • 🇨🇦 CanadaLead💰 CA$156,800 - CA$294,000 per yearWebsite

Tech Stack

Tools & technologies

NumpyPandasPythonPyTorchTensorflow

About the role

Key responsibilities & impact

Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks
Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives
Lead the effort in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy
Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context
Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps
Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment
Drive technical execution with high autonomy, making critical design and implementation decisions independently

Requirements

What you’ll need

BS in Software Engineering, Computer Science, or equivalent field of study
8+ years of postgraduate experience in software development
Experience developing highly-available, performant, scalable ML systems, including large-scale data processing pipelines
Strong expertise in Python (including the ML stack: PyTorch, TensorFlow, JAX, NumPy, Pandas)
Long, successful history of driving the full ML lifecycle: from initial data exploration and hypothesis testing to architecture, model training, evaluation, and production deployment
Deep proficiency in MLOps and software best practices, including CI/CD for ML, experiment tracking (e.g., Weights & Biases, MLflow), automated testing, and version control for both code and datasets

Benefits

Comp & perks

Comprehensive medical, dental, and vision coverage for employees and dependents with generous employer premium contributions
Retirement savings with company matching
Paid parental leave
Inclusive family-building benefits
Flexible paid time off
Company-wide seasonal breaks
Support for flexible work arrangements that enable sustainable performance
Opportunities for continuous learning and growth through on-the-job development, cross-functional collaboration, and access to internal learning and development programs

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

data pipelinesML modelsalgorithmshyper-parameters tuningmodel architecture optimizationPythonMLOpsCI/CDautomated testingversion control

Soft Skills

leadershipcollaborationcommunicationautonomyproblem-solvingcritical thinkingengineering standardscode qualitysystem efficiencysecurity