Staff Machine Learning Engineer, AI Generation Engine

Entangled Future; Kanada; Remote; CAD 180.000 - 260.000; Vollzeit

Benefits dieser Stelle

Health benefits

Competitive compensation

Zusammenfassung

SandboxAQ in Canada (remote) seeks a Staff Machine Learning Engineer to own end-to-end ML lifecycle from data exploration and model development to scalable production deployment.

You will design data pipelines, develop novel ML models, and rigorously test them for production readiness while collaborating with AI researchers, product managers, and software engineers to ship AI-first SaaS products using Large Quantitative Models.

Qualifikationen

• BS in Software Engineering, Computer Science, or equivalent
• 8+ years of postgraduate software development experience
• Experience building highly available, scalable ML systems
• Strong Python expertise (ML stack: PyTorch, TensorFlow, JAX, NumPy, Pandas)
• Full ML lifecycle experience: data exploration to production deployment
• Deep proficiency in MLOps and software best practices (CI/CD, experiment tracking, version control)
• MS or PhD in Software Eng, CS, or equivalent
• Experience with cloud platforms (GCP, AWS) and scalable software development

Aufgaben

• Design, build, and maintain robust data pipelines for training and evaluation of large models
• Develop, test, and optimize ML models and algorithms with measurable performance
• Engineer feature extraction from large datasets to boost model accuracy and efficiency
• Analyze model behavior, tune hyperparameters, and optimize production deployments
• Collaborate with researchers, product managers, and SWEs to translate business goals into ML roadmaps
• Uphold high engineering standards for code quality, security, and reliability in prototyping
• Drive technical decisions with autonomy across multiple teams

Kenntnisse

Python

ML lifecycle

Data pipelines

Cloud platforms

MLOps

Collaboration

Software architecture

Production deployment

Ausbildung

BS in Software Engineering or CS

MS or PhD in Software Engineering or CS

Tools

PyTorch

TensorFlow

JAX

MLflow

Weights & Biases

Git

Jobbeschreibung

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)