Top Machine Learning Jobs in Ogden, UT

The AI/Machine Learning Specialist designs and implements AI solutions, collaborates with cross-functional teams, and continuously improves ML processes.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

The Technical Product Manager will develop and manage the cloud infrastructure for AI, collaborating across teams to enhance AI solutions and service offerings.

The Product Manager, Growth connects product teams with market insights, conducts customer interviews, organizes feedback, and supports go-to-market strategies.

The Principal AI/ML Researcher/Engineer will lead research and development of reasoning, planning, and decision-making systems, integrating multi-agent coordination with reinforcement learning to enhance Airbnb's AI capabilities for personalized experiences.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

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

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

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

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

The Staff Machine Learning Engineer will lead ML initiatives, develop AI systems, optimize pipelines, and mentor team members to automate healthcare processes.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

Lead the design and operation of Twilio's ML and data platform, architecting cloud-native pipelines and implementing MLOps best practices while mentoring engineers and collaborating across teams.

Design, build, and scale enterprise-grade Generative AI and ML platforms (RAG, vector search, agent orchestration, feature/embedding stores). Implement cloud-native infrastructure, observability, compliance-by-design (HIPAA/CLIA/GDPR), and reusable APIs/SDKs. Lead architecture, mentor engineers, and productionize multi-agent and LLM workflows for clinical and business applications.

The Customer Success Engineer will lead deployments, serve as a technical advisor, and support customer engagement, focusing on product enhancements and user experience.

Seeking a Senior Researcher to evaluate and optimize video datasets for training generative video models, ensuring data quality and effectiveness through analysis and characterization.

The Staff Product Manager will define product strategy, lead the product lifecycle, collaborate across teams, and drive innovation in claims processing software.

Lead technical solution development for military and Federal civilian health capture and proposal efforts, ensuring technical compliance and producing quality architecture designs.

Build and maintain internal web tools supporting ML workflows. Develop backend services, frontend features, and deployments; participate in on-call rotations and code reviews; collaborate with partner teams to scale ML tooling and improve authentication and deployment processes.

Lead end-to-end technical programs for ML infrastructure (inference, serving, efficiency, endpoints). Coordinate cross-functional teams, prioritize overlapping projects, manage timelines, budgets and scope, drive incident management and continuous improvement, and communicate status to engineering and leadership.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

The role involves applying established ML techniques to cybersecurity data problems, integrating ML outputs into data pipelines, and ensuring model reliability. Collaboration with engineers and product teams is key, along with strong communication skills and understanding of data flow.

Lead the engineering organization for data and AI platforms, making architectural decisions and driving product vision while managing teams and aligning with business priorities.

Lead the strategy and implementation of AI solutions in R&D, driving adoption and improving operational efficiency through advanced analytics. Collaborate with cross-functional teams to design AI/ML models and evaluate new technologies.

The role involves leading ML client engagements, shaping strategies, advising on ML architecture, and influencing project outcomes, while guiding teams and driving business value.