Top Hybrid Machine Learning Engineer Jobs

Build and maintain AI-powered, time-aware forecasting and pattern-discovery models for front-office rates trading. Apply statistical learning and time series techniques, productionize ML/AI models, and collaborate with trading, risk, and governance teams to integrate predictive intelligence into business processes.

Lead design, build, and scale of production ML systems (including supervised, unsupervised, and LLM-based models). Translate prototypes into cloud-native deployments, optimize model performance, implement MLOps practices, and collaborate cross-functionally to integrate AI features while promoting model governance, explainability, and responsible AI.

Build and iterate machine learning models and proofs-of-concept using TrainingPeaks' athlete and coach data. Collaborate with product and engineering, debug and monitor models, interface with customers for feedback, and stay current on applied ML and AI techniques to deliver production-ready solutions.

The Senior Machine Learning Engineer will develop and enhance Moveworks' NLU and agentic AI capabilities, deliver production-quality code, and solve complex engineering challenges in conversational AI.

Lead the architecture and implementation of a scalable AI/ML platform, focusing on data pipelines and integration of ML capabilities in business systems. Mentor and collaborate with cross-functional teams to optimize performance and deliver business value.

The Machine Learning Engineer will design, implement, and manage complex ML algorithms, focusing on computer vision and data analysis while collaborating with team members to transition algorithms to production.

The Staff ML Engineer will design and implement backend components for the ML Inference Platform, ensuring efficient model serving and collaborating with ML teams to enhance AI infrastructure at GM.

As a Principal Quantitative Modeler, you will develop econometric and machine learning models for credit risk management, providing insights and solutions across Capital One's business. Responsibilities include partnering with different teams, applying quantitative methods, and delivering clear communication and documentation of modeling processes.

Lead a team to build large-scale machine learning indexing and ranking systems while collaborating with product teams and enhancing team performance.

Design and implement AI capabilities, transition prototypes to production, build agent workflows, optimize data retrieval, and automate evaluation processes.