Top Machine Learning Jobs in Provo, UT

Design, own, and scale end-to-end ML training and inference pipelines for on- and off-vehicle systems. Build extensible software across vehicle platforms, evaluate new models, grow training data scale, collaborate with ML and robotics engineers, and drive an E2E-first approach for deployment and field operations.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

As VP of Engineering, you'll lead the application engineering strategy and team, integrating AI capabilities into products while ensuring quality and execution across backend, mobile, and desktop platforms.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

The role involves product management of AI tools, collaborating with research, engineering, and design teams to develop and enhance products, focusing on user needs and market value.

The Staff Machine Learning Engineer will enhance Quora's recommendation systems, lead machine learning projects, and own end-to-end ML processes.

The role involves improving recommendation systems using machine learning, developing end-to-end ML systems, and collaborating with engineers.

The DevOps Engineer will design, build, and optimize ML and data infrastructure on GCP, mentor a team, and ensure system reliability and performance.

The Lead Machine Learning Engineer will optimize AWS SageMaker, manage ML pipelines, develop infrastructure as code with Terraform, build CI/CD pipelines, and collaborate with data scientists to deploy models in production.

The role involves leading ML strategy, architecting systems, designing algorithms, collaborating with teams, and mentoring engineers in AI for enterprise analytics.

The AI/ML Engineer develops AI features for Cengage's educational products, focusing on enhancing student engagement and learning outcomes through shipping production AI capabilities.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

The Customer Success Manager will drive user adoption and value realization for ISP customers, manage onboarding and retention, and collaborate cross-functionally to enhance customer satisfaction and business growth.

Intern will help create and run automated tests across mobile, partner integrations, marketing sites, web frontend, and performance testing for Rocket Copilot. Work includes building automation scripts, executing test cases, measuring performance, and participating in daily scrum with mentorship from senior engineers.

Lead and mentor engineering teams while owning platform architecture (React, Node/Express, Postgres, AWS). Drive TypeScript migration, design ETL and AI/LLM integration frameworks, ensure reliability and observability, manage delivery, incidents, and hiring, and define technical strategy for production ML/AI systems.

The Junior AI/ML Engineer will deliver machine learning projects, handle Python programming, and learn from a senior team member, focusing on real impact initiatives.

The Head of AI will define and execute an AI roadmap, lead research initiatives, mentor the AI team, and collaborate with senior leadership to enhance health technology.

As a Senior Software Engineer, you'll build and operate the ML platform, optimizing data operations, managing the feature store, and supporting ML initiatives while driving architectural roadmap decisions.

The Staff Engineer will architect and build AI and ML infrastructures, mentor engineers, and lead high-impact initiatives to enhance ML capabilities across Attentive's platform.

Develop novel machine learning methods to enhance computer vision solutions, identify areas for innovation, and translate research into scalable production systems.

The Machine Learning Solutions Architect designs and implements data solutions, overseeing model deployment and ensuring robust performance and integration within customer environments.

Develop AI/ML-powered features in ClickHouse Cloud, focusing on frontend and API architecture, UI/UX, and integrations with AI systems.

Build and maintain MLOps pipelines, deploy and monitor ML models as scalable services, transform and integrate healthcare data (FHIR/HL7), ensure reproducible feature management, and uphold HIPAA/HITRUST security for production ML in clinical systems.

As a Senior AI Engineer, you will develop and optimize Generative AI applications, manage AI pipelines, collaborate with clients, and ensure production delivery with MLOps best practices.