Top Remote Machine Learning Jobs in Ogden, UT

As a PhD Systems & AI/ML Research Intern, you'll work on infrastructure problems, build prototypes, run benchmarks, and document findings.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

Lead the development of audit practices, enhance methodologies, integrate data analytics, ensure compliance, mentor teams, and communicate effectively with leadership.

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.

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

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.

As a Software Engineer at Speechify, you'll enhance AI Voice products, optimize ML pipelines, and improve deployment tools in a fast-paced environment.

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.

Lead end-to-end product initiatives focused on AI-powered features, collaborating with cross-functional teams to define and ship products.

The Technology Director leads technology strategy and delivery across client engagements, directly contributing to technical solutions, architecture, and hands-on production work while mentoring teams and representing technology in client partnerships.

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.