Top Machine Learning Jobs in Salt Lake City, UT

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.

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.

Design and implement computational workflows to investigate homogeneous catalytic cycles, support catalyst discovery and optimization with industry partners, develop large-scale quantum chemistry datasets and ML models, collaborate with platform and engineering teams to improve discovery workflows, lead client engagements, and mentor junior scientists while communicating results via reports and publications.

Lead credit analytics for the Send Now Pay Later product: build and own valuation and vintage models, manage credit policies, monitor portfolio health and NPL trends, integrate ML scorecards, and evaluate alternative data to optimize approvals and loss performance.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, valuation and NPV models, vintage analysis, executive reporting, model integration and testing, and alternative data integration to optimize approval rates and portfolio health.

Lead credit analytics for the Send Now Pay Later product: build and maintain credit policies, valuation and NPV models, vintage analysis, portfolio surveillance, executive reporting, model integration/testing, and alternative data exploration to optimize approval rates and manage portfolio risk.

Serve as the primary technical resource for channel partners to enable partner sales through training, demos, PoV engagements, joint account strategy, and creation of repeatable technical assets and reference architectures. Influence product roadmap via partner feedback and represent 7AI at events to scale partner-led pipeline and long-term customer success.

As a Data Scientist, you will build predictive models, develop software tools, and analyze geoscientific data to support mineral exploration and discovery.

The Data Scientist will develop predictive models, analyze geophysical data, and collaborate with geoscientists to enhance exploration programs, focusing on mineral deposits.

The Machine Learning Engineer will develop editable visuals combining computer vision and machine learning, enabling chat-driven figure editing and leveraging biological understanding to produce accurate visuals.

AVIA seeks experts in Digital Health and AI for advisory roles to support healthcare systems' technological transformations as part-time, project-based consultants.

Lead and scale full-stack engineering teams for Yahoo Mail; define engineering vision and roadmap; drive AI-native development and AI transformation using Generative AI/LLMs and agents; partner with Product, Design, and Data Science to deliver scalable cloud-native mobile and web experiences; hire, mentor, and retain engineering talent while guiding architecture, technical reviews, and platform modernization.

Lead and grow a small ML engineering team while staying hands-on: design and ship GenAI features, build agentic LLM systems (NER, classification, retrieval, summarization), partner with PM and engineering, scope ambiguous problems, and reduce COGS through ML automation.

The role involves developing and deploying machine learning models for forecasting within the Camus platform, requiring collaboration and technical depth in ML.

Design, operate, and scale ML data and training pipelines for autonomous driving. Build distributed training, orchestration, CI/CD and infrastructure-as-code; maintain data/metadata stores and tooling to support large-scale model training and evaluation across cloud and cluster environments.

The Senior Machine Learning Engineer will build and own production ML systems, manage end-to-end workflow, debug issues, and mentor others.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

As a Python and Kubernetes Software Engineer, you will develop data analytics solutions using open source tools, collaborating with a distributed team.

Develop AI-powered solutions for personalized content and marketing. Collaborate with teams to optimize ML models and pipelines at scale, mentoring engineers and driving strategic growth initiatives.

Design and scale machine learning systems and infrastructure, partner with data and applied scientists, build reusable ML pipelines and abstractions, ensure production readiness using robust software engineering practices, and prototype new technologies to improve customer experience and automate business processes.

Seeking a Senior Machine Learning Engineer to design and implement ML systems, optimize deployment, and collaborate with data teams, focusing on scalability and performance.

The Machine Learning Engineer will design, build, evaluate, and deploy ML systems, collaborating on projects in safety, security, and intelligence applications, while owning projects from initial research through to production.

Define and execute predictive modeling and ML strategy for credit risk, servicing, and collections. Lead cross-functional initiatives, oversee model development, deployment, and monitoring, advise senior leadership on data strategy, represent analytics in regulatory and audit forums, and mentor peers while fostering organizational partnerships.

Lead and execute ML/AI strategy, design and deploy production-grade ML and GenAI solutions, mentor MLEs, collaborate with cross-functional teams, and build scalable, secure ML/LLM platforms and inference pipelines to drive business impact.

Lead end-to-end design, delivery, and lifecycle management of production AI/ML solutions for strategic clients. Translate business requirements into scalable, secure ML architectures, drive deployments, monitoring, governance, and mentor teams while collaborating with stakeholders across sales, engineering, and data science.