Top Machine Learning Jobs in Ogden, UT

As a Senior Machine Learning Engineer at Samsara, you'll develop ML solutions for physical operations, driving new initiatives and optimizing systems. Collaborate with engineering teams to create end-to-end ML applications while influencing industry practices and contributing to company culture.

The Senior Machine Learning Engineer will design and maintain ML infrastructure, develop and deploy machine learning models, monitor performance, collaborate with teams, and mentor junior engineers at Kikoff, a consumer fintech startup focused on credit-building solutions.

The Real-Time Cloud Data Integration Engineer will design and develop real-time data integration solutions, ensuring data integrity and efficient processes. Responsibilities include working with cloud technologies, analyzing and troubleshooting SQL queries, collaborating with stakeholders, and maintaining existing data integration tools.

As a Staff Machine Learning Engineer, you will design and develop scalable ML platforms and services, define and maintain Machine Learning data pipelines, productionize models, optimize performance, automate workflows, and promote best software engineering practices. You'll collaborate with cross-functional teams to drive ML roadmap and architectural decisions while improving productivity through shared tools.

As a Machine Learning Optimization Evangelist, you will educate ML practitioners on optimizing their workloads, promote Hugging Face's integrations with AI partners, publish technical content, and engage in community-building efforts. You will engage with well-known AI companies to enhance their use of Hugging Face tools and platforms.

As a Machine Learning Engineer at Pinterest, you will build technologies to personalize user experiences, improve machine learning models across various product surfaces, and keep up with industry trends in recommendation systems while leveraging large-scale data and quick experimentation.

As a Senior Machine Learning Engineer, you'll bridge the gap between data science and ad systems engineering, developing solutions for model experimentation, monitoring, and pipelines, while driving innovation and best practices in machine learning systems.