Top Hybrid Machine Learning Engineer Jobs

The AI Engineer role involves optimizing language models for fraud detection and building backend infrastructure to support scalable operations for risk management and as well as developing efficient machine learning solutions.

The Machine Learning Engineer will prototype and evaluate LLM features, collaborate with teams, contribute to technical design, and measure success metrics.

The AI Enterprise Engineer will work with enterprise customers to define model specifications, analyze agent behavior, and benchmark models using Bespoke Labs tools, requiring a strong foundation in AI/ML and production deployment.

The role involves developing advanced machine learning algorithms, solving complex optimization problems using reinforcement learning techniques, and leading technical projects while optimizing solutions for operational efficiency.

The Lead Machine Learning Engineer will design and develop ML evaluation systems, translate research to production, and mentor engineers. They must engage with various teams and stay updated on ML advancements.

Develop and implement machine learning models for evaluating disputes and chargebacks, and create a model orchestration layer. Collaborate on data use for enhanced user experiences.

Drive company growth by integrating core technologies with partner platforms. Collaborate with teams to create scalable ML systems and mentor junior engineers.

The Staff Machine Learning Engineer will architect and scale ML pipelines for clinical decision-making, ensuring technical standards and global collaboration.

The Senior ML Engineer will develop LLM-based applications, predictive models for patient outcomes, and evaluation frameworks to optimize AI in healthcare.

The ML/AI Engineer will define machine learning direction, develop automated pipelines, integrate models, and collaborate with product teams to solve user problems.