Top Machine Learning Jobs in Salt Lake City, UT

Lead and extend EnergyHub's platform for managing distributed energy resources and VPP dispatch controls. Conduct customer and market research, define and prioritize features, validate hypotheses, respond to client data requests, and collaborate with product, engineering, data science, sales, and client success to deliver strategic and incremental product improvements.

Own OEM-facing product roadmap for the Automotive Enterprise Cloud, translate OEM requirements into scalable integrations (APIs, data pipelines, reporting), lead discovery and delivery with cross-functional teams, define success metrics, produce product artifacts, and drive OEM adoption and satisfaction.

This role involves leading strategic planning, product innovation, and cross-functional collaboration to enhance pharmacy analytics and drive growth initiatives for CVS Health.

As Director of AI/ML, lead the ML organization, set technical vision, mentor a high-performing team, and contribute to model development and production deployment.

The Director of Data Sciences leads a team in strategic initiatives, drives machine learning and AI solutions, and manages resources to enhance data-driven decision making across the business unit.

The AI & Data Science Director will lead AI strategies, foster technical leadership, support team development, and integrate generative AI into products.

Lead development of an AI-enabled Ethereum Layer 2 platform: build front-end and back-end systems, deploy and optimize AI models, create data pipelines, collaborate on model training, and contribute to product innovation and other company projects.

Serve as primary post-sales relationship owner for federal customers, driving onboarding, adoption, and retention of HiddenLayer cybersecurity/AI solutions. Coordinate cross-functional teams, conduct business reviews, mitigate risks, capture customer feedback, and support renewals and expansion in complex federal environments.

Sell HiddenLayer's AI security platform to federal agencies by developing account strategies, driving pipeline and revenue, leading technical and executive engagements, navigating federal acquisition and contracting, coordinating cross-functional teams, and supporting adoption, expansion, and renewals across the Intelligence Community, DoD, DHS, and civilian agencies.

The Manager of RevOps/Yield analyzes patterns and data to optimize ad operations, manage vendor relationships, and oversee a remote team for revenue generation.

Embed with clients to gather requirements and design AI-driven solutions for fraud and fincrime. Architect, train, evaluate, and deploy custom AI agents and workflows, optimize model performance, establish risk metrics and evaluation pipelines, and collaborate with engineering/product teams to scale into production.

As Software Engineering Manager, you'll lead a team in enhancing content workflows through automation and AI, develop scalable solutions, and manage cross-functional collaboration.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

The Senior Generative AI Engineer will design and deploy AI/ML solutions, focusing on Generative AI systems and their operational excellence across various sectors like R&D and Clinical Trials.

The GTM role focuses on increasing revenue through new sales and partnerships, understanding client needs, and communicating product value.

Lead a team to develop groundbreaking technology combining NLP, big data, and AI to enhance digital communication and interaction.

Build and maintain full-stack product features, backend services, APIs, data pipelines, and AI integrations to transform customer interaction data into AI-powered experiences. Collaborate with cross-functional teams to design, test, and support production systems, internal tools, and user-facing interfaces. Own ambiguous problems end-to-end and raise engineering quality across the data and AI platform.

Lead full-cycle enterprise sales into Fortune 500 retailers, marketplaces, and travel companies. Manage eight-figure deals with C-suite and technical buyers, coordinate internal resources, build procurement-ready ROI/business cases, run pilots/A-B tests, and drive land-and-expand growth while shaping GTM playbooks.

Design, build, and scale low-latency distributed platform components; define technical vision and architecture; collaborate with product and ML teams; evaluate build-vs-buy decisions; prototype research breakthroughs and deliver robust, production-grade services.

Lead healthcare and pharmaceutical marketing analytics: analyze CRM, media, healthcare and behavioral data; build segmentation, attribution, audience scoring, and predictive models; develop measurement frameworks; use Python, SQL, BI and automation; translate insights for executives; mentor juniors and partner with Strategy, Media, and Client Services.

Lead definition and delivery of AI-powered capabilities for a sustainability real-estate data platform. Shape roadmap, partner with customers and data science, own end-to-end features, define evaluation and trust frameworks, and ensure data foundations support AI. Drive cross-functional alignment and use AI tools in product workflow.

Develop, test, and maintain applications using Java, Python, or Node.js. Write clean, efficient code, debug and optimize, collaborate with cross-functional teams, use data structures/algorithms, work with SQL/NoSQL databases, participate in code reviews, and stay current on AI/ML concepts, agents, and automation tools.

Lead and grow the Client Data Science team (including three direct reports); mentor staff; write and optimize R, Python, and SQL code; apply machine learning and statistical methods; aggregate and analyze multi-source data; create visualizations and reports; liaise with stakeholders; document work in JIRA and present findings to diverse audiences.

The AI/ML Data Contributor will engage in tasks to enhance machine learning models through data labeling, evaluation, and testing, working flexibly on various projects.

Manage a team of data scientists to develop fraud detection models, lead planning and collaboration with leadership, and communicate outcomes effectively while maintaining strong business intuition and technical leadership.