Staff Machine Learning Engineer, AI Generation Engine

SandboxAQ is a high-growth company delivering AI solutions that address some of the world's greatest challenges. They are seeking a highly accomplished Machine Learning Engineer to take ownership of the end-to-end ML lifecycle, focusing on designing and rapidly building AI-first products that incorporate Large Quantitative Models (LQMs) and sophisticated agentic frameworks.

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)