Top Machine Learning Jobs in Salt Lake City, UT

Build and design backend services, AI agents, and machine learning models. Collaborate cross-functionally to deliver AI features and maintain code quality.

The Senior ML Data Scientist will develop machine learning models to detect fraud, manage the data science lifecycle, and collaborate with teams on strategies.

The Principal Machine Learning Engineer will define technical direction, architect scalable systems, lead cross-organizational initiatives, mentor engineers, and communicate effectively at all levels.

Build and improve machine learning models and systems to classify and enrich Internet data, collaborating with engineering, research, and product teams.

As a Staff Data Scientist, you will lead initiatives to build and optimize machine learning models, collaborate across departments, and solve complex business problems using data.

Lead the development and implementation of machine learning models for energy trading optimization and forecasting, while overseeing a team of data scientists and engineers.

Lead architecture and engineering for production-grade ML in healthcare: design MLOps CI/CD, scalable low-latency inference for LLMs and classical models, build reliable data pipelines for FHIR/HL7/DICOM, manage cloud IaC and observability, ensure HIPAA/HITRUST compliance, and mentor engineering teams to operationalize agentic AI workflows.

The role involves building bioinformatics and ML pipelines, analyzing multi-omics data, mentoring, and collaborating across teams to develop actionable insights and scientific breakthroughs.

As a Senior MLOps Engineer, you will manage production ML systems, define lifecycle strategies, optimize ML pipelines, and collaborate with cross-functional teams to enhance ML operations.

Design, develop, and deploy machine learning models; analyze large datasets; build scalable data pipelines and distributed computing environments; integrate ML solutions with cross-functional teams; optimize performance and support production ML infrastructure.

The role involves improving AI agent quality through designing experiments, maintaining evaluation infrastructure, optimizing LLM inference, and collaborating with product teams. Responsibilities include end-to-end ownership from research to deployment in a dedicated ML Engineer context.

Design, train, deploy, and optimize ML models and pipelines for large-scale, safety-critical systems. Build data ingestion and MLOps infrastructure, validate model reliability, co-author technical reports, and collaborate with engineers and customers to integrate new data sources. Some customer travel required.

The Senior Machine Learning Engineer will design and operate advanced ML systems for immersive experiences, enhance workflows, and lead projects while mentoring peers.

Design and develop machine learning systems for construction robots, including scalable infrastructure, computer vision models, and data analysis for performance improvement.

The Machine Learning Engineer will design and build systems that improve the AI shopping agent by using real user feedback, focusing on feedback loops and metrics to enhance user interactions and agent performance.

Design and operate production ML systems for large-scale streaming data, ensuring reliability and performance while collaborating with researchers to integrate models into distributed systems.

Lead design, development, and productionization of advanced ML models and data pipelines. Drive architecture, experimentation, validation, and best practices while mentoring ML engineers and delivering scalable, business-driving AI solutions.

Lead and grow a global data engineering and ML organization, design cloud-native data platforms and pipelines, deliver ML-powered products, establish engineering and MLOps best practices, and support sales through solutioning and client-facing technical leadership.

Work with the product team on automating data ETL pipelines, deploying ML models, and connecting ML models to user interfaces. Collaborate with cross-functional teams on product development.

Manage and mentor a team of machine learning and data engineers, overseeing ML model development, data pipelines, and collaborating with product teams on technical requirements.

Design and deliver customer experience solutions using machine learning techniques, analyze customer data, implement ML algorithms, and automate processes.

As a Senior Machine Learning Engineer, you will design and maintain ML systems for content recommendations, search, and spam detection, while augmenting user experience and system performance.

The role involves preparing traffic data, developing and deploying machine learning models on Google Vertex AI, and collaborating with teams to improve project workflows.

The Expert Product Architect AI/ML will design technical foundations for Managed AI Services, ensuring scalable and secure integrations of AI capabilities and operational frameworks within Ensono's offerings.

Lead the Data Platform & ML Ops team, overseeing data infrastructure and ML lifecycles, mentoring engineers, and driving product innovations.