Top Hybrid Machine Learning Engineer Jobs

As a Software Engineer (AI), you will design and implement AI systems, manage backend services, and collaborate closely with teams. You need extensive experience with LLMs and backend technologies.

In this role, you'll design algorithms for real-time bidding systems, enhance forecasting capabilities, and engage in A/B testing while collaborating with cross-functional teams.

The role involves designing and optimizing bidding algorithms for real-time bidding systems, leveraging machine learning and large datasets to enhance performance and maximize advertiser returns, while collaborating across teams.

The Senior ML Engineer will build and maintain large-scale ML production pipelines, focusing on delivering production-grade solutions and collaborating with R&D teams. Responsibilities include prototyping new algorithms, managing datasets, and optimizing ML models for customer impact.

Design, implement, and support AI/ML systems, working with business units to optimize workflows and educate staff. Communicate findings to stakeholders and ensure compliance with policies.

Conduct model development and implementation for risk models, support sales initiatives, analyze transaction data with big data technologies, and promote analytics education at Visa.

The ML Scientist will develop machine learning models for climate risk, collaborate across teams, and validate algorithms to improve predictions of weather extremes.

The Senior Machine Learning Engineer will develop and maintain ML infrastructure, deploy models, collaborate with teams, and mentor junior engineers.

The Staff Machine Learning Engineer will lead the development of advanced AI models, collaborate with teams, conduct research, and mentor junior engineers.

The Machine Learning Engineer will design, deploy, and operate ML systems on AWS while ensuring reliability, cost-effectiveness, and compliance for the gaming industry.

The role involves integrating and optimizing machine learning models for autonomous vehicle scenario generation, collaborating on AI-driven safety validation, and contributing to data-driven metrics and tooling.