Top Senior Machine Learning Jobs in Provo, UT

Own the strategy, requirements, evaluation, and reliability of AI agent products. Partner closely with ML, backend, and mobile engineers to make trade-offs across quality, latency, cost, reliability, and user experience. Define offline and online evaluation frameworks, drive product execution, and ensure AI features are correct, predictable, safe, and trusted. Translate model capabilities and feedback into product decisions, roadmaps, and rapid iteration.

Lead the Data Sources product roadmap for document parsing, fraud detection, and bank aggregation. Drive metrics (parse accuracy, fraud catch rate, connection success, SLAs), write specs, engage clients, mentor PMs/engineers, and represent the platform externally. Prioritize document types, extraction models, reliability, and scalability to improve conversion, client satisfaction, and operational efficiency across mortgage, tenant screening, and government use cases.

Lead end-to-end ML systems for identity verification, including document authenticity, face match, liveness and deepfake detection. Build GNN-based identity graphs, behavioral/device-intelligence models, vendor benchmarking/evaluation, and production enforcement. Mentor engineers and align ML architecture with Product, Compliance, Risk, and Security stakeholders.

Build and deploy production-grade LLM applications, AI agents, models, and scalable RAG pipelines for investment and business workflows. Architect secure MCP integrations with enterprise systems, establish MLOps and LLMOps practices, optimize model performance and latency, and develop evaluation, monitoring, governance, and rollback processes. Collaborate with data engineering and platform teams to provide secure, governed access to organizational data.

Lead development of safety frameworks and V&V for end-to-end autonomous robotics, perform risk assessments and safety reviews, derive technical safety requirements, and coordinate cross-functional teams to monitor fleet performance and deploy scalable autonomy.

Lead design and implementation of scalable data models and analytical tooling for Consumer Product. Partner cross-functionally to translate business needs into reliable SQL/Python data products, dashboards, and automated pipelines using engineering best practices (CI/CD, testing, version control, monitoring). Drive data-informed strategy and manage long-term infrastructure while supporting ad hoc requests.

Own full-cycle recruiting for technical roles, partner with hiring managers to define roles, source and close candidates, build scalable systems and pipelines, optimize hiring with data, and support a global, remote people team while acting as a culture ambassador.

As a Senior Machine Learning Engineer, you'll build and deploy ML models for risk and fraud detection, leading technical efforts and collaborating cross-functionally.

The Director of AI will lead AI and machine learning initiatives, develop AI architecture for humanoid robots, oversee production-ready AI systems, and manage team growth and budget.

Design and deliver enterprise-grade generative AI solutions on AWS using Bedrock, AgentCore, SageMaker, LLMs, RAG pipelines, vector databases, and agentic workflows. Build scalable APIs and integrations, optimize prompts and models, evaluate performance, and implement security and governance. Collaborate with application, data, and platform teams, troubleshoot production systems, define best practices, and mentor engineers while remaining hands-on.

Develop and expand software products and platforms across backend, frontend, cloud, AI, and machine learning systems. Build efficient, reliable, and testable code; collaborate with business partners to launch features; influence product and engineering decisions; mentor team members; and help shape team culture and product vision in an agile environment.

Own product strategy, vision, roadmaps, backlogs, and delivery for enterprise SaaS products serving financial services organizations. Translate strategy into requirements and user stories, lead Agile ceremonies, engage customers, monitor product metrics, manage delivery risks, and align portfolio companies with leadership and cross-functional teams. Identify and deliver AI-enabled capabilities, workflow automation, analytics, and intelligent business solutions.

Build and productionize customer-facing data and AI solutions on the Databricks platform. Own architecture and design decisions, develop end-to-end data pipelines and applications, integrate machine learning models, and guide enterprise customers through implementation and adoption. Collaborate with engineering, product, support, project managers, and customer stakeholders while delivering projects, documenting solutions, resolving issues, and creating reusable frameworks. Travel to customers approximately 20% of the time.

Design, build, and integrate AI-enabled applications using C#/.NET and Azure. Develop data ingestion, transformation, and automation pipelines for structured and unstructured data. Implement and optimize LLM/Generative AI solutions, build APIs, and support end-to-end delivery from design through production handoff while partnering with business stakeholders.

Design and deploy production recommendation and retrieval engines, build data orchestration pipelines for multiple modalities, implement agentic workflow frameworks, and prototype, optimize, and scale models with monitoring, governance, and low-latency SLAs.

Develop and deploy deep learning models for AI-guided antibody drug discovery and target identification. Design experiments, build systems biology models, and create in silico and in vitro validation approaches. Collaborate with biological and wet-lab teams, present findings, publish high-impact research, and mentor scientists and engineers.

Leads engineering managers and software teams building customer-facing front-end applications and internal and external APIs. Sets technical direction, oversees delivery, manages risks and dependencies, improves engineering quality and security, develops talent, supports hiring, and partners with product and design. The role drives innovation and scalable software solutions for connected vehicle and fleet operations.

Leads the engineering strategy, architecture, development, operations, and continuous improvement of an enterprise agentic AI foundation. Establishes scalable engineering standards, operating models, governance, and reusable platform services that enable AI products and intelligent workflows. Partners with product, data, infrastructure, security, and business teams to modernize existing systems, guide cross-functional delivery, monitor operations, and communicate priorities, risks, and investment needs to senior leaders. Manages and develops product or engineering leaders while driving measurable business value.

Designs, evaluates, and improves ranking and recommendation systems through experimentation, feature engineering, and production machine learning. Builds reliable data and feature pipelines, incorporates multimodal signals, manages uncertainty, calibration, and model drift, and monitors system performance. Partners cross-functionally with Engineering, Product, Business, and Analytics to deliver scalable, latency-sensitive solutions with measurable business impact.

Lead data science for Root's digital partnerships channel, managing a team to drive growth, profitability, experimentation, forecasting, optimization, and measurement. Partner with cross-functional teams to improve acquisition economics, influence product and technical roadmaps, deliver executive reporting, and establish scalable operating processes.

The Senior Director leads research in Application Platforms & AI, guiding clients in AI strategy and market trends, while producing critical research content and engaging with clients directly.

The Senior Machine Learning Engineer will develop core ML models, manage data pipelines, and collaborate with stakeholders to deliver scalable ML solutions.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Contract role combining actuarial science, risk management, and AI to improve portfolio performance. Responsibilities include loss and combined ratio analysis, portfolio risk assessment, catastrophe and exposure modeling, stress testing, and integrating ML to enhance pricing, reserving, and risk selection while collaborating with underwriting, finance, and product teams.

Lead vision, strategy, and roadmap for Fetch's referral growth engine. Own end-to-end referral lifecycle, build incentives, experimentation, AI-driven personalization, and cross-functional initiatives to drive MAU growth and durable network effects.