Top Senior Machine Learning Jobs in Salt Lake City, UT

Lead data quality and data management for oncology studies, overseeing DM teams, vendor/CRO management, and compliance with CDISC/ICH-GCP. Develop risk management, integrate and surveil multi-source data, advise stakeholders on data collection and standards, produce metrics and dashboards, manage project resourcing and finances, review contracts, and deliver training to ensure high-quality, regulatory-compliant clinical and real-world data.

Lead systems engineering for the firm's computing and data infrastructure including datacenter, cloud, storage, networks, OS, and databases. Oversee automated testing, monitoring, ML-driven anomaly detection, incident management, performance tuning, disaster recovery, and change management to ensure application stability and service performance.

The Staff Machine Learning Engineer will design and build scalable ML systems, mentor engineers, and drive AI strategy at SailPoint, focusing on AI-powered identity security solutions.

The Vice President of AI will define and lead the AI strategy at Tempo, integrating AI capabilities into products, building teams, and ensuring ethical use of AI while driving business outcomes and customer value.

Lead a team of advanced product analysts, conduct data analysis and statistical modeling, design experiments, and develop reporting tools to drive product decisions and business performance.

Support development of pricing arrangements, financial models, budgets and AFAs for a law firm. Produce pricing analyses, competitive market research, and performance metrics. Collaborate with attorneys, IT, and data teams to implement pricing tools (Elite 3E, Iridium), leverage AI/analytics for forecasting, and train stakeholders on pricing technology.

Lead technical customer engagements and guide cross-functional teams to design, build, and deploy enterprise-scale AI solutions. Provide architecture and program leadership for LLMs, RAG, ML, and cloud-native systems; establish engineering standards, CI/CD, observability, and AI governance; mentor engineers and support business development.

Lead customer-facing design, development, and deployment of AI-enabled software solutions. Translate stakeholder needs into scalable architectures, build production-quality code and APIs, implement CI/CD and observability, and mentor engineering teams. Drive solution discovery, technical leadership across projects, and adoption of modern AI/ML and DevOps practices to deliver mission-critical outcomes.

Lead and grow a Sales Engineering team focused on industry expansion. Recruit, mentor, and train SEs; build industry-specific collateral and processes; collaborate cross-functionally with Sales, Product, CS, and Marketing; optimize workflows to drive revenue and support complex, AI-enabled GTM solutions.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.

The Senior Product Manager will lead the integration of airline and third-party partners, optimize supply metrics, and leverage AI for automation. Responsibilities include managing APIs, collaborating with engineering, and improving the booking experience.

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.

The Senior Machine Learning Engineer will design, prototype, build AI systems, evaluate ML models, and improve Vectara's AI Agents with a focus on reliability and performance.

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 VP of Sales will drive enterprise adoption of AI-powered healthcare software, engage with stakeholders, and shape go-to-market strategies for new AI products.

As a Machine Learning Engineer, you'll design, build, and maintain models to detect security threats, ensuring model efficiency and aligning initiatives with business goals.

Lead the vision and strategy for the Commerce Intelligence Platform, transforming data assets into scalable, revenue-generating products, fostering cross-functional collaboration and driving market growth.

Lead Yahoo's Data Activation product area: define vision and strategy, partner with Data Science and Engineering, drive cross-vertical data activation initiatives, deliver measurable business impact, and mentor/manage product teams to execute on audience, personalization, and targeting use cases.

This role leads the ML Acceleration team, focusing on enhancing ML model training efficiency and team management, while driving technical strategy and collaboration.

Lead the Performance ML team at MNTN, driving strategy and deployment of predictive models for ad technology, while managing, mentoring, and scaling engineering teams.

Lead delivery of advanced analytics and investigative support for federal programs. Manage multiple concurrent projects, schedules, costs, risks, personnel, vendors, and stakeholder communications to ensure quality, timely contract-aligned deliverables.

The Data Science Manager will lead and mentor a team of data scientists, drive data-driven decision-making across Stripe, and collaborate with various stakeholders. Responsibilities include managing projects, recruiting, and contributing to broader data science initiatives.

Design, build, and operate production AI platform capabilities and agentic workflows. Collaborate on architecture, write production-quality backend code, mentor developers, troubleshoot production issues, and drive adoption of reusable AI frameworks, MLOps practices, and cloud-native intelligent services across a multi-tenant SaaS dental platform.

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.

Embed with customers to discover needs, design and implement production-grade integrations (APIs, data pipelines, event-driven workflows), deploy and troubleshoot solutions, train IT teams, and drive adoption and measurable business outcomes.