Top Machine Learning Jobs in Columbus, OH

Lead pre-sales technical engagements for Conversational and Generative AI solutions, collaborate across teams, and design value-driven demos for enterprise customers.

Lead the Data Science & POCs team to optimize fraud analytics and decision systems, managing projects, mentoring staff, and collaborating with teams to reduce fraud losses.

Lead the strategy and execution of AI-powered neuro solutions at Viz.ai, collaborating cross-functionally and driving commercialization efforts.

The role includes developing B2B marketing strategies, collaborating with sales, creating targeted materials, and conducting market research to support growth.

Lead a small engineering team in developing and deploying core product functionality, ensuring high quality and security standards. Collaborate with cross-functional partners while managing and scaling the team effectively.

The Principal Partner Marketing Manager will lead strategic co-marketing initiatives, develop joint marketing plans, execute integrated campaigns, enable field teams, and collaborate cross-functionally to drive awareness, demand, and revenue growth through partnership with Microsoft.

As a Machine Learning Engineer, you'll evaluate neural network quality, coordinate with researchers, manage experiments, and report results to streamline AI software development.

The Product Manager will lead platform offerings, engage with cross-functional teams, define product metrics, and ensure the integration of intelligent machines for customers.

The role involves leading a team to design, develop, and implement AI-driven applications on the ServiceNow platform while managing all aspects of application development and integration with other systems.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

Lead the Data & Analytics team, focusing on building data platforms, improving delivery speeds, and integrating Generative AI into data strategies.

Lead the Data Science team, overseeing the development and deployment of ML solutions, managing analytics workflow, and collaborating with cross-functional teams.

As a Lead AI Engineer, design and implement AI and machine learning models and pipelines to enhance healthcare outcomes and efficiency.

As a Senior Machine Learning Engineer, you'll create ML models to combat fraud on Airbnb, collaborating across teams to enhance user safety and confidence.

As a Full Stack Engineer, you'll develop production-ready AI and ML features, build APIs, design scalable systems, and implement frontend solutions using Angular.

As a Senior Machine Learning Platform Engineer, you'll architect a scalable ML platform, collaborating with teams to ensure compliance, optimize performance, and guide best practices in ML workflows.

Build a next-generation ML data and feature infrastructure platform to enhance the productivity of ML practitioners and improve feature accessibility.

The Software Engineer L5 will design, build, and maintain developer productivity tools for machine learning teams, requiring strong software engineering and design skills with a focus on large CI/CD systems.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

Join Netflix's Algorithms team as a Machine Learning Software Engineer to design and develop solutions for personalization systems. Collaborate on scalable machine learning algorithms and enhance software engineering practices.

As an ML Engineer, you will build and productionize predictive models for ad inventory and campaign performance, contributing to the growth of Netflix's ad platform.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

The Staff Machine Learning Engineer will develop ML models and infrastructure for personalized experiences, collaborating with teams to implement end-to-end solutions and ensure high quality in systems development.

Design and implement ML infrastructure for scalable deployment, collaborate across teams, optimize ML systems, and enhance tooling.

Drive cross-team engineering efforts to improve user experience on Homefeed by implementing large-scale machine learning systems and enhancing data processing efficiency.