Top Machine Learning Jobs in Provo, UT

Conduct applied AI and machine learning research focused on multimodal data, foundation models, agents, evaluation, and data enrichment. Monitor research trends, prototype emerging techniques, design experiments, validate opportunities, and translate findings into briefs and recommendations. Partner with product and engineering teams to guide data strategy, training corpus generation, and longer-term opportunity prioritization.

Leads technical sales engagements for identity verification and fraud prevention solutions. Responsibilities include discovery, solution design, technical workshops, proof-of-concept development, product demonstrations, and advising enterprise clients. Partners with Sales, Product, Data Science, Engineering, and Customer Success; translates technical capabilities into business value; provides domain expertise in fraud, risk, compliance, and regulated industries; contributes to thought leadership; and mentors junior consultants.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

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 clinical infrastructure and program portfolio powering Arine’s medication management platform. Evolve the clinical recommendation engine, expand data inputs, standardize program configuration, and create repeatable frameworks for launching and scaling clinical programs. Partner with clinical, engineering, data science, implementation, and customer success teams to translate needs into requirements, improve onboarding, maintain regulatory alignment, and prioritize work using clinical safety, user value, and business impact.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Design and develop scalable backend systems using Kotlin, Micronaut, and Spring. Build RESTful APIs, optimize PostgreSQL and Elasticsearch databases, implement RabbitMQ-based asynchronous processing, integrate AI tools, and create automated tests. Deploy and monitor services on GCP using Docker, Kubernetes, and Terraform. Collaborate cross-functionally, participate in code reviews, troubleshoot performance issues, and deliver reliable backend products.

Leads engineering for one or more product-domain teams, partnering with Engineering Managers, Product, Data Science, Analytics, and Insurance stakeholders. Responsibilities include setting technical strategy and roadmaps, coaching engineers, driving cross-team initiatives, contributing code, leading incident response and resiliency improvements, and establishing engineering standards. Requires strong Ruby on Rails expertise, Rust familiarity, software leadership experience, and excellent communication skills.

Lead client engagements involving AI and machine learning solutions. Translate customer outcomes into technical direction, define requirements and success measures, review architectures and data pipelines, coordinate engineering delivery, and manage project planning, budgets, forecasts, and workstreams. The role requires technical fluency across APIs, cloud platforms, CI/CD, and ML deployment, plus experience delivering custom software and data solutions for complex, multi-stakeholder customers.

Supports the development, testing, and evaluation of AI/ML models for educational products. Responsibilities include data collection and cleaning, feature engineering, model validation, analyzing outputs, identifying optimization opportunities, documenting findings, preparing technical reports, and collaborating with Data Science, Engineering, and Product teams. The intern also explores applications in personalized learning, content recommendations, adaptive learning, NLP, generative AI, and responsible AI.

Leads complex, cross-functional Payment Integrity programs from strategy through execution. Develops plans, roadmaps, milestones, reporting, and executive communications; manages risks, dependencies, stakeholders, and decision points. Coordinates data, analytics, technology, vendor, and emerging AI initiatives using Agile planning practices. Drives accountability, continuous improvement, and successful delivery across teams without direct authority.

Leads enterprise real-world data strategy at Bristol Myers Squibb, translating R&D and business priorities into data investment, acquisition, evaluation, and evidence-generation strategies. Serves as the team’s AI innovation lead, identifying applications for generative AI, machine learning, and advanced analytics. Evaluates datasets, vendors, partnerships, and emerging technologies; develops executive-ready business cases; and drives cross-functional alignment across strategy, data, governance, customer success, and R&D teams.

Leads development and deployment of statistical, machine learning, and causal inference models for customer decisioning. Designs experiments, estimates treatment effects and incremental value, evaluates targeting performance, and integrates analytical solutions into governed production systems. Partners with business and technology teams, communicates complex findings, and continuously improves models using experimental and operational results.

Leads enterprise technology strategy, architecture, modernization, security, reliability, AI adoption, and execution. The VP will oversee engineering and data organizations, develop technical leaders, establish roadmaps and performance standards, guide cloud and data platform evolution, and connect technology investments to measurable business outcomes. This executive partners with the CEO and cross-functional leaders while managing organizational performance, operational risk, governance, resilience, and technology investments.

Claims Counsel advises on auto insurance coverage, multi-state tort law, civil procedure, and litigation matters. Responsibilities include drafting coverage opinions, researching insurance laws, supporting claims policies and training, collaborating with legal and claims teams, conducting adjuster training, and drafting form letters and releases.

Own the strategy and lifecycle of machine learning models powering maritime domain awareness products. Translate customer and operational needs into model roadmaps, requirements, metrics, evaluation standards, and release criteria. Collaborate with ML engineers on experimentation, error analysis, and deployment decisions. Lead cross-functional execution from ideation through launch, establish feedback loops for monitoring and retraining, and communicate technical tradeoffs to executives, customers, and partners.

Lead the demand generation function by translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Own HubSpot automation, Salesforce integration, lead scoring, funnel analytics, marketing-sourced pipeline, budgets, and agency performance. Collaborate with Sales and Commercial Operations to improve lead handoffs and conversion. Apply AI and automation to increase execution efficiency while coaching team members and building the future demand generation team.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

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.