Top Machine Learning Jobs in Ogden, UT

Manage global technical support teams for MongoDB, focusing on customer satisfaction through technical issue resolution, performance enhancement, and team development.

Lead the development of machine learning systems for Cedar's Personalization Engine, focusing on ML modeling, MLOps, and team management to drive healthcare solutions.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

As a Risk Manager, you'll analyze and mitigate security risks, implement fraud defenses, and collaborate with cross-functional teams to enhance security objectives.

The Head of Consumer Product will create engaging experiences using relational AI, focusing on user well-being rather than metrics like engagement. Responsibilities include product strategy, user research, gamification, and community building.

Lead a team of ML engineers to develop computer vision models, manage ML roadmaps, and improve product functionalities through innovative technologies.

Lead machine learning initiatives to enhance underwriting model accuracy. Design and implement advanced training strategies and optimize workflows. Mentor engineers and influence modeling strategy.

Lead the development and execution of ML platform strategies, manage a high-performing team, and enhance 3D deep learning applications.

This role involves designing and building advanced ML models to protect against fraud and enhance user experience while mentoring lower-level engineers and influencing technical roadmaps.

The role involves designing, developing, and deploying machine learning models for healthcare applications, collaborating with cross-functional teams, and optimizing model performance.

Develop and optimize machine learning models for data extraction from tax documents, collaborate on software integration, and mentor junior team members.

Lead design and implementation of enterprise-grade AI systems, architect AI infrastructure, and drive technical strategy for AI-powered products primarily for military applications.

As a Senior Machine Learning Engineer, you will develop scalable ML solutions, collaborate with teams, optimize models, and implement best practices for deployments and infrastructures.

The Senior Applied Scientist II is responsible for designing and implementing ML models to optimize the supply chain, collaborating across teams, and driving data-driven decision-making.

The Senior Machine Learning Engineer will develop ML APIs, build data pipelines, and collaborate across teams to enhance ML models and applications.

As a Senior Machine Learning Engineer, you will design ML infrastructure, lead zero-to-one initiatives, collaborate on projects, and enhance scalability in the development of ML applications.

The role involves leading Gartner's AI technology research, advising clients on AI applications, managing generative AI solutions, and providing actionable insights on AI trends and best practices.

The role involves producing insights for AI product management, advising clients on AI strategy, and developing business approaches for AI technologies. The analyst will work closely with executives and product managers, driving AI investments and guiding product lifecycle management.

Lead GenAI/AI initiatives by collaborating with cross-functional teams to identify opportunities, ensure ethical practices, and develop scalable solutions. Communicate effectively with stakeholders and support successful project transitions into products.

The role involves leading the Revenue ML team, managing engineers focused on building AI/ML capabilities for user and revenue growth, overseeing project execution, and fostering collaboration among cross-functional teams.

The Associate Director leads the Global Records and Information Management team, optimizing governance practices, managing projects, and developing relationships across various stakeholders, while ensuring regulatory compliance and strategic implementation.

The role involves handling complex technical support cases, acting as a senior escalation point, managing bugs, mentoring peers, and improving support quality.

The Technical Account Manager (TAM) ensures technical success for Genesys customers by building relationships, managing adoption, and providing technical expertise.

The Digital Inside Sales Specialist will identify and close digital transformation opportunities in AI, ML, and automation, focusing on B2B sales within targeted accounts while collaborating with internal teams for technical consultation.

The Partner Sales role involves managing partnerships to drive revenue, developing strategic partner plans, and collaborating across teams to achieve sales targets.