Staff Machine Learning Engineer, AI Generation Engine

Staff Machine Learning Engineer, AI Generation Engine at SandboxAQ, Canada (remote). A lead-level full time position in the AI Generation Engine team, listed under software engineering.

The Opportunity

The AI Generation Engine (SAIGE) team is responsible for rapidly designing, prototyping, and validating AI-first SaaS products that leverage SandboxAQ’s Large Quantitative Models (LQMs) and emerging agentic frameworks. The team operates at high velocity, bridging cutting-edge AI research and production-grade software to unlock new use cases across the company.

SandboxAQ's AI Generation Engine (SAIGE) team is seeking a highly accomplished Machine Learning Engineer to take ownership of the end-to-end ML lifecycle, from initial data exploration and model development to scalable production deployment. This role is central to designing and rapidly building AI-first products that incorporate Large Quantitative Models (LQMs) and sophisticated agentic frameworks.

We are looking for a hands-on engineer who is passionate about owning the entire lifecycle of model development. This requires significant industry experience in bringing machine learning models from conception and experimentation to production and deployment in a robust, scalable manner, including (but not limited to): Data Acquisition and Curation, Infrastructure, Pre-Training, Evaluations, and Fine-Tuning. This person will be one of the founding engineers to join the SAIGE team and will be the bridge between cutting-edge AI concepts and functional, real-world MVPs.

As a Machine Learning Engineer on the SAIGE team, your primary goal will be to rapidly iterate on different potential solutions to build and evaluate new models, focusing on speed and tangible outcomes. You'll be part of a diverse team consisting of software engineers, ML experts, products managers and user experience researchers, where they will play a key role in efficient and effective enablement of the cutting‑edge technologies being developed at SandboxAQ.

Why Join Us? We offer a comprehensive and competitive benefits package designed to support your health, financial well‑being, and life outside of work.

Responsibilities

• 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

• 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.
• MS or PhD in Software Engineering, Computer Science or equivalent experience
• Financial simulation or technical experience, risk simulation
• Equivalent experience includes tech leadership in a complex space, driving technical design and execution cross‑collaboratively across multiple teams and organizations
• Experience with scalable software development on cloud computing platforms (e.g., GCP, AWS)