Top Remote Machine Learning Jobs in Ogden, UT

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Designs, develops, deploys, maintains, and optimizes AI and machine learning solutions. The consultant will build NLP and computer vision models, analyze datasets, troubleshoot applications, use Azure data services and Synapse Analytics, and improve model performance. Responsibilities include collaborating with technical teams and business stakeholders, documenting solutions, presenting findings, and occasionally traveling to Middletown, Pennsylvania for orientation, meetings, implementations, or system issues.

Leads new business development, account growth, commercial discussions, client relationships, and sales operations for AI and GenAI data solutions. Partners with engineering, delivery, and customer success teams to understand frontier lab needs, structure projects, close opportunities, and expand client accounts. Requires quota-based technology sales experience, startup experience, STEM education, and strong AI/ML market knowledge.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

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.

Develop and maintain production machine learning models, build scalable training and serving technologies, optimize real-time and batch ML pipelines, and monitor model performance and drift. The role involves deep neural networks, recommendation systems, automated retraining, ML research, engineering excellence, and communicating technical concepts to diverse audiences. Candidates need 6+ years of industry ML experience, strong coding skills, and experience deploying neural networks at scale.

Own audience or measurement products for Fetch’s monetization business, from problem framing through delivery. Build predictive models, datasets, population inferences, and causal measurement studies using receipt, partner, panel, survey, and contextual data. Partner with Product, Engineering, Sales, and GTM to productionize analyses, communicate methods and limitations to customers, and improve products using campaign outcomes, holdouts, and fresh data.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Develop and deploy machine learning, AI, causal inference, and statistical solutions for advertising measurement products. Build prototypes, conduct experiments, analyze large-scale data, and advance attribution capabilities from early concepts through production. Collaborate with engineering, product, analysts, and account teams to improve measurement and marketing ROI. Responsibilities include owning measurement products, applying Bayesian and marketing models, writing production code, and communicating methodologies and findings to technical and nontechnical audiences.

Owns product lifecycle management, market analysis, customer feedback, product roadmaps, NPI processes, launches, and revenue initiatives. Uses Generative AI, LLMs, predictive analytics, and AI agents to improve research, backlog prioritization, documentation, and competitive intelligence. Collaborates with technical and business stakeholders, mentors junior employees, supports distribution strategy, delivers product training, and oversees new product development. The role is remote within the United States and requires 8+ years of experience.

Lead the vision, strategy, and evolution of LTK’s creator experience and commerce products. Define creator workflows, identify AI-powered opportunities, analyze behavioral data, establish outcome metrics, and drive experimentation to improve engagement, retention, earnings, and product quality. Collaborate across Product, Engineering, Design, Data, and Creator Success while influencing senior stakeholders and guiding complex, multi-quarter initiatives.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build product enablement for global go-to-market teams across AI Agents, APIs, and Creative products. Responsibilities include preparing launch workshops, competitive training, product certifications, onboarding and everboarding content, hands-on demos, role-play simulations, practice environments, and performance measurement. The role partners with product marketing, engineering, research, and enablement teams while using ElevenLabs’ AI technology to create training experiences.

Develop generative AI solutions using Python, AWS, LLMs, chatbots, prompt and context engineering, RAG, vector databases, and agentic AI frameworks. Build coding prompts and contexts with GitHub Copilot, Claude Code, and Amazon Q Developer to convert business stories into production code and pull requests. Design autonomous and multi-agent workflows, memory and token-management systems, caching strategies, MCPs, and real-time AI decisioning features. Auto-lending experience and A/B testing expertise are advantageous.

Owns CSL Plasma’s target-state business architecture, capability models, value streams, architecture decisions, integration standards, and technology roadmaps. Translates business strategy and regulatory requirements into architecture blueprints across digital products, while governing AI, automation, data, and integration patterns. Partners with product, engineering, enterprise architecture, security, privacy, and compliance teams to drive committed designs, architectural consistency, and migration sequencing across a complex regulated environment.

Leads engineering standards, architecture governance, platform strategy, technical quality, security and compliance engineering, and production AI governance across CSL Plasma’s digital products. Manages cross-domain technical teams, sets cloud and integration direction, owns shared engineering platforms and data architecture, and ensures reliable, scalable, regulated software delivery. The role also drives AI-assisted development, production AI operations, engineering metrics, talent development, and alignment with enterprise architecture and cybersecurity.

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.

Collects, processes, and analyzes data from multiple sources using R, Python, and SQL. Develops reports, visualizations, analytical methods, and data-driven insights while applying statistical and introductory machine learning techniques. Collaborates with internal and external stakeholders, documents project progress in JIRA, debugs code, clarifies requirements, and communicates findings through reports and presentations.

Define the vision, strategy, and roadmap for AI-powered products. Lead use-case evaluation, requirements, experimentation, development, deployment, adoption, and continuous improvement across Generative AI, LLM, RAG, and agentic solutions. Collaborate with engineering, data science, design, and business stakeholders; establish success metrics; manage AI risks; and communicate product value, progress, and strategy to leadership and clients.

Lead enterprise data and AI architecture across marketing, customer engagement, digital, sales, and analytics. Design scalable cloud data platforms, reusable data products, semantic models, real-time and batch architectures, governed AI capabilities, machine learning systems, generative AI, agents, recommendations, and personalization. Establish standards for governance, security, privacy, quality, lineage, and observability. Provide technical direction, lead architecture reviews and roadmaps, partner with engineering and data science teams, and mentor architects and engineers.

Leads Blue Team capabilities for large-scale federal cyber exercises. Designs defender workflows, hunt content, evaluation criteria, training objectives, and operational playbooks; coordinates customers, red teams, control cells, and account teams; facilitates exercises and after-action reviews; translates findings into detection improvements and remediation plans; and mentors cybersecurity personnel. The role requires cloud, hybrid environment, threat hunting, detection engineering, incident response, and cybersecurity exercise expertise, plus extensive customer engagement and 50–75% overnight travel.

Lead the architecture, development, deployment, and reliability of cloud-based customer experience platforms using AWS, Genesys Cloud, and Salesforce Service Cloud. Design integrations, data pathways, CI/CD pipelines, and infrastructure automation while ensuring security, compliance, scalability, and performance. Serve as a technical subject matter expert, guide global agile teams and vendors, coach engineers, establish DevOps practices, and troubleshoot critical production issues.

Own the design, adaptation, integration, release, and production rollout of tailored AI solutions for a large client. Collaborate with distributed engineers, contribute client-specific changes to a shared codebase, ensure reliability in a regulated environment, serve as the client’s technical contact, and resolve complex production issues independently.

Lead enterprise security operations, resilience, incident response, threat intelligence, cloud security, IAM, and risk management. Serve as deputy to the CISO and primary incident commander while advising executives on cyber risk. Build and scale multidisciplinary security teams, modernize cloud-native defenses, embed security-by-design across products and AI/ML systems, and establish metrics-driven governance, automation, regulatory alignment, and business-focused security strategies.