Top Machine Learning Jobs in Ogden, UT

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

The AI Sales Executive drives new business growth and expands client relationships by selling AI-driven solutions for engineering and manufacturing sectors, managing the full sales cycle with a consultative approach.

As a Senior Technical Product Manager, you will lead the AI Platform team to define roadmaps, collaborate with technical experts, and improve healthcare delivery by leveraging AI technologies.

Lead program delivery for engineering teams at Arine, optimizing internal processes and ensuring timely execution of technical programs while facilitating Agile methodologies.

The Senior Technical Architect will lead AI SDLC transformations, create architectural solutions, mentor teams, and drive innovation in regulated industries.

As a Regional Sales Director, you'll lead a sales team, develop business plans, and drive sales growth by managing complex cycles and nurturing talent.

Lead and scale a global Deal Desk to structure complex SaaS transactions, own deal review and approval workflows, improve quote-to-cash across CRM/CPQ/CLM/billing/ERP, implement AI/ML for efficiency, provide analytics on discounting and deal quality, and build a high-performing global team while advising Sales and executives on strategic deals.

Lead the data and ML product vision: define roadmap, prototype models on noisy behavioral datasets, translate signals into user-facing features, partner with engineering to productionize ML, and generate insights to inform product roadmaps.

Lead and deliver complex, cross-team AI platform programs: manage schedules, risks, integration of AI workflows and agents, enable operational readiness, drive TPM best practices, and communicate status and metrics to stakeholders in a remote, multi-timezone environment.

Design, build, and manage data pipelines, integrations, ML and rules-based classification, and applications. Collaborate with data scientists and cross-functional Scrum teams, drive technical strategy, innovate practices, and mentor junior engineers.

Own AI-powered transaction features for document intelligence and workflow automation. Lead discovery with brokerages, define roadmap, write specs, run pilots, measure impact, and ensure compliance integration across product teams.

Lead a cross-functional team to develop medical coding software and improve a chart confidence pipeline. Translate stakeholder requirements, guide data scientists and engineers, evaluate recommendations, and report progress to internal stakeholders to increase automation and chart readiness.

Own Oscilar's AML product suite (transaction monitoring, KYC/KYB, screening, case management, SAR filing, AI agents). Define detection rules, model calibration, data strategy, and defensible audit trails. Collaborate with engineers and compliance, use AI across discovery/design/analysis, deliver PRDs, validate models, and ship production features.

Lead end-to-end research applying advanced ML/DL (transformers, GNNs, RL, ensembles) to generate alpha across digital asset markets, build production-grade pipelines, prevent leakage/overfitting, analyze microstructure and on-chain data, and mentor junior researchers.

Own and implement end-to-end product features across the stack (React/TypeScript front end, Ruby on Rails backend, relational DBs, GCP infra). Design APIs and data models, ensure performance and reliability, integrate AI/LLM-powered features, collaborate with product/design/clinical teams, perform code reviews, and mentor junior engineers.

The Senior Director of Data Science & AI leads AI strategy execution, managing a multidisciplinary team to develop and govern AI solutions, with a focus on innovation, operations, and ethical standards.

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

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.

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.

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.

Lead post-acquisition member lifecycle strategy and execution across onboarding, activation, engagement, cross-sell, and retention. Architect journeys, build and run automated email/push/SMS programs, define segmentation and measurement frameworks, synthesize analytics for optimization, and champion AI-driven marketing innovations while managing multi-client priorities and mentoring teams.

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.

Analyze historical demand, occupancy, transactions and competitor pricing to optimize daily, monthly, and event pricing. Build dashboards, deliver revenue insights to leadership, and collaborate with Operations and Marketing to implement pricing and promotional strategies while maintaining data integrity.

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.