Staff/Principal Machine Learning Engineer

As a Staff/Principal Machine Learning Engineer, you will operate at the intersection of applied machine learning and platform engineering, helping scale ML innovation across a high-impact technology environment. You will design foundational tools, infrastructure, and workflows that accelerate model development and improve predictive performance. The role spans the full machine learning lifecycle, from data preparation and feature engineering through training, evaluation, deployment, and monitoring. You will work closely with research scientists, data scientists, ML engineers, and product teams to turn complex modeling needs into scalable solutions. Your work will help automate repetitive processes, improve experimentation speed, and enable scientists to focus on high-value modeling and analysis. This is a high-influence position for an experienced ML engineer who enjoys technical leadership, cross-functional collaboration, and solving ambiguous problems at scale.

• 5–7+ years of hands-on experience in applied machine learning, with substantial exposure to production-scale modeling.
• Strong theoretical and practical foundation in machine learning and statistics, including the ability to reason about model assumptions, bias, uncertainty, tradeoffs, evaluation, and failure modes.
• Deep understanding of how machine learning models work beyond the abstractions of common frameworks, with the ability to apply this knowledge to production systems.
• Demonstrated expertise across the end-to-end model development lifecycle, including data preparation, feature engineering, training, evaluation, and deployment.
• Experience working in high-scale, ML-driven product environments, particularly in fintech, pricing, risk modeling, or similarly complex domains.
• Strong proficiency in Python and core machine learning frameworks such as PyTorch, TensorFlow, Scikit-learn, and XGBoost.
• Ability to operate autonomously and provide technical direction in ambiguous, high-impact environments.
• Experience partnering with ML scientists, engineers, product teams, and other cross-functional stakeholders.
• Strong ability to bridge scientific and engineering disciplines and influence technical strategy across teams.
• Master’s degree or PhD in a quantitative discipline, or equivalent additional professional experience.
• Strong numerical reasoning, analytical ability, and comfort working at a fast pace.
• Practical experience with CUDA/GPU acceleration is preferred.
• Experience with feature store architecture, embedding systems, or synthetic data generation is a plus.
• Proven experience improving model accuracy in production with measurable business outcomes is preferred.
• Familiarity with modern experimentation frameworks, hyperparameter optimization, and automated model selection techniques is advantageous.