Staff Machine Learning Engineer – AI Generation Engine

About the role

The AI Generation Engine (SAIGE) team at SandboxAQ is looking for a Staff Machine Learning Engineer to own the full ML lifecycle—from data acquisition and model development to scalable production deployment—building AI‑first SaaS products that leverage Large Quantitative Models and emerging agentic frameworks.

Key responsibilities

• Design, build, and maintain robust data pipelines for training, validation, and continuous retraining of LQMs and agentic frameworks.
• Develop, implement, and rigorously test novel ML models and algorithms, defining metrics aligned with product objectives.
• Clean, transform, and engineer features from large‑scale datasets to optimize model performance.
• Analyze model behavior, tune hyper‑parameters, and optimize architecture for efficiency and accuracy in production.
• Collaborate with AI researchers, product managers, and software engineers to translate business goals into actionable ML roadmaps.

Required profile

• BS in Software Engineering, Computer Science or equivalent; 8+ years of post‑graduate software development experience.
• Proven experience delivering highly‑available, performant, scalable ML systems and large‑scale data processing pipelines.
• Deep expertise in Python and the ML stack (PyTorch, TensorFlow, JAX, NumPy, Pandas).
• Full‑lifecycle ML experience from data exploration to production deployment, including MLOps, CI/CD, experiment tracking and version control.

Required skills

• Python
• PyTorch
• TensorFlow
• JAX
• NumPy
• Pandas
• MLOps (CI/CD for ML, experiment tracking – Weights & Biases, MLflow)
• Version control for code and datasets
• Cloud platforms – GCP, AWS

What we offer

• Competitive base salary, performance‑based incentives and equity participation.
• Comprehensive medical, dental and vision coverage, retirement savings with company match, paid parental leave and family‑building benefits.
• Flexible paid time off, seasonal breaks and support for remote or flexible work arrangements.
• Opportunities for continuous learning, cross‑functional collaboration and internal development programs.