Top Remote Machine Learning Jobs in Salt Lake City, UT

Serve as a technical post-sales advisor for enterprise accounts: drive product adoption, manage technical relationships, advocate customer needs to product and support teams, recommend enhancements, assist architecture and onboarding, and support account expansion with Sales. Requires travel ~25% and fluency in English plus Portuguese or Spanish.

Serve as the technical lead in pre-sales for state, local, and education customers: run discovery, design POCs and workshops, deliver demos, map needs to Socure solutions, advise on fraud/risk/compliance, influence product, and mentor junior consultants.

Lead product strategy and roadmap for enterprise data platforms, BI, analytics, and AI/ML products. Align investments with business priorities, partner with engineering and data teams, ensure governance and responsible AI, measure adoption and business impact, and build and mentor cross-functional product teams to drive platform adoption and value.

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.

The Senior Machine Learning Engineer will design, implement, and optimize machine learning models, drive AI initiatives, and collaborate cross-functionally to enhance SailPoint's AI capabilities.

Lead the Interventions engineering pillar owning technical strategy and execution for early diagnosis, longitudinal care, and treatment. Scale data and ML infrastructure, improve clinical data accuracy, manage ML/LLM features, drive AI-assisted SDLC, and partner cross-functionally with Product, Data Science, and Clinical Operations to meet clinical performance goals.

Design, build, and operate production-grade AI systems using ML, LLMs, RAG, and agentic workflows. Define evaluation strategies, implement data/retrieval/orchestration layers, apply MLOps/LLMOps, and partner with stakeholders to prioritize, monitor, troubleshoot, and improve AI solutions balancing quality, latency, cost, reliability, and maintainability.

Lead analytic efforts for Humana In-Home Programs: build member-level predictive models, forecasting, segmentation, and impact evaluations to improve engagement, close care gaps, and support HEDIS Stars performance. Partner with business, ops, and tech teams to deploy cloud-based ML solutions and present insights to senior leaders.

The Machine Learning Researcher will design datasets and evaluate environments for agentic systems, focusing on data quality and benchmarking model performance in RL settings. They will collaborate with teams to translate workflows into evaluative frameworks and improve the understanding of high-quality datasets.

The Customer Solutions Manager enriches client relationships by driving data adoption, providing analytics insights, and identifying growth opportunities. Responsibilities include client engagement, data storytelling, and advocating for client needs internally.

The Staff GenAI Backend Engineer leads engineering for intelligent routing systems, collaborates with teams to enhance customer support, and maintains architectural integrity in a high-availability environment.

The Product Manager will drive the lifecycle of AI-driven IoT solutions, manage backlogs, collaborate with teams, and gather customer feedback to enhance product quality.

The Engineering Manager will lead teams in developing and managing Blackbaud's Data & AI platform, focusing on scalability, security, and cost-efficiency.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

The role involves developing and integrating AI features into Cribl's products, collaborating with teams, and maintaining operational efficiency.

Lead Data Scientist responsible for creating ML-driven risk assessments and AI workflows within a SaaS security platform, ensuring effective decision-making and system reliability.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

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

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead credit analytics for the Send Now Pay Later product: build and optimize valuation, vintage and NPV models; manage credit policies, portfolio monitoring, and NPL reporting; integrate and test scoring models with engineering/data science; and research alternative data to improve predictive performance.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

Lead quantitative credit analytics for a Send Now Pay Later (SNPL) product: build and maintain credit policies, pricing and NPV models, vintage analysis, portfolio monitoring, model integration/testing, and alternative data evaluation to optimize approval rates and manage portfolio health.