Top Machine Learning Jobs in Kansas City, MO

Develop, optimize, and deploy scalable ML solutions for content decisions at Netflix, utilizing advanced ML techniques and collaboration with teams.

Design, build, and scale ML systems for demand forecasting and vehicle positioning. Collaborate with teams and mentor engineers to enhance ML expertise.

As a Machine Learning Engineer, you'll optimize and deploy production ML models, build inference services, and collaborate closely with teams to integrate new models and enhance performance.

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

Implement and optimize machine learning features for 3D models, collaborating with research and engineering teams and ensuring reliable deployment processes.

Design and develop scalable machine learning systems for fraud insights, collaborating with teams to deliver high-quality ML infrastructure effectively.

Lead the design and implementation of deep learning models for biological data, focusing on model architecture, project leadership, and innovation in machine learning applications in biology.

The role focuses on building scalable backend systems for ML features, collaborating on model adaptation, ensuring production reliability, and API design.

Design and implement software solutions for machine learning operations, focusing on geospatial imagery analytics and MLOps software development.

Design and implement backend ML systems for personalized feeds at Reddit. Mentor teams, drive technical discussions, and improve MLOps tools.

Lead the design and development of machine learning infrastructure for robotics, defining strategies, collaborating with teams, and ensuring best practices in MLOps.

As a Principal ML Engineer, you will design ML architecture, tackle technical challenges, mentor engineers, and influence ML strategy across the company.

Develop and support ML infrastructure for large-scale scientific workflows, improve automation processes, and contribute to product features in AI-enabled drug discovery.

The ML Scientist will lead data science and ML projects, automate reporting, support product development, and maintain ML infrastructure at Harvard Business Publishing.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Lead high-impact machine learning projects, developing systems for advertising optimization, collaborating cross-functionally, and implementing statistical techniques.

The Sr. Staff Machine Learning Engineer will lead architecture and development of ML systems, enhance patient interactions, optimize treatment recommendations, and mentor engineers.

The role involves designing and maintaining ML infrastructure, ensuring performance and security, collaborating with ML engineers, and implementing modern AI capabilities.

Lead the Safety ML team at Discord, developing AI/ML infrastructure, collaborating on safety challenges, and mentoring engineers while driving technical strategy and innovation.

As a Senior Data Scientist, you will collaborate across teams to enhance data science, analyze models, and improve product features to aid student writing.

Lead the development of data science solutions for inventory and logistics planning, optimize planning processes, and deploy predictive models. Collaborate across functions and present insights to stakeholders.

The role involves developing cloud solutions and enhancing ML/AI pipelines, ensuring service availability, and automating deployments in a scalable environment.

Lead the design and deployment of scalable ML systems, mentor engineers, and collaborate on impactful ML solutions in production environments.

The Software Engineer (Full Stack) will develop and deploy clinical AI/ML products, ensuring product quality and collaborating with cross-functional teams.

Lead the design and deployment of scalable ML infrastructure, focusing on MLOps and LLM strategies, while mentoring team members and ensuring operational maturity.