Top Machine Learning Jobs in Salt Lake City, UT

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead and build a high-performing Solution Engineering team for the strategic enterprise segment. Recruit, mentor, and train sales engineers, collaborate cross-functionally with Sales, Product, and GTM teams, optimize processes and collateral, support deal execution, and embody company culture and operating principles while occasionally engaging directly on deals.

As a Staff Machine Learning Engineer, you will design AI products on Edge devices, optimize ML model performance, and work with large-scale datasets, collaborating across teams to deliver impactful solutions.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

As a Systems Architect, you'll define and drive the infrastructure architecture for AI/ML workloads, design multi-cloud strategies, manage compute orchestration and storage systems, and lead capacity planning, while also collaborating with engineering leadership.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.

Lead product strategy and execution for data and AI platforms, driving GenAI/LLM initiatives, cross-functional collaboration, responsible AI governance, experimentation (A/B testing), measurement of model impact, and mentoring product teams to embed scalable ML capabilities into clinical and member experiences.

Own pre-sales technical engagement: consult customers, deliver technical presentations and demos (web, iOS, Android), lead POCs, support RFP/RFI responses, manage onboarding and integrations, and advise sales, product, and engineering teams on implementation and value realization.

Design, build, and scale backend systems and APIs for high-transaction consumer products. Collaborate with product, credit, compliance, support, and analytics teams. Contribute to code reviews, technical planning, security, and operational excellence. Mentor peers and promote engineering best practices.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Lead initiatives in designing and building large-scale distributed data systems and ML infrastructure, while collaborating across various engineering teams.

Design, implement, and scale ML-based solutions for search and advertising; define data collection, evaluation, and production serving strategies; analyze data to inform development; communicate cross-functionally; produce technical reports; and stay current with advances in LLMs, vision models, and generative AI.

Design, build, and ship production AI agents integrated end-to-end into a TypeScript/Node.js/React stack. Apply ML judgment to product decisions, improve agent reliability and evaluations, and move quickly in a startup environment owning ambiguous problems.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Remote internship to build and deploy data and AI solutions for aerospace engineering. Responsibilities include prototyping ML/LLM/CV systems, architecting distributed data platforms, coding demos, presenting architectures, and shipping production-ready solutions while collaborating across teams.

Build and deploy machine learning models on large healthcare datasets, extract actionable insights, develop scalable data processing systems, collaborate with cross-functional teams, and communicate findings to stakeholders to improve patient outcomes.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

The Enterprise Account Executive will drive revenue by engaging with enterprise insurance clients, managing the sales cycle, and fostering strategic relationships with executives, leveraging Vellum's data analytics platform.

Lead development of new credit line management programs by designing, testing, and scaling policies. Own end-to-end analytical work using SQL/Python, partner with Data Science and Product, and present data-driven recommendations to senior stakeholders to improve spend recovery, utilization, and line-management performance.

Lead development of AI-powered solutions, architect large language models, capture domain knowledge, and deliver client-focused insights. Manage a team to integrate AI capabilities across products.

The job involves selling AI and Machine Learning technologies to mortgage companies, managing the sales process for enterprise customers, and working with remote teams.