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
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