Top Machine Learning Jobs in Salt Lake City, UT

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

The VP of Marketing will lead the marketing ecosystem to drive growth, define competitive advantages, influence product design, and mentor a diverse team towards a mission-driven culture.

The SAP Innovation Architect will design SAP automations, mentor engineers, and drive innovation in AI-driven SAP operations, while ensuring scalability and security in integrations.

The Customer Success Manager drives the adoption of AI solutions in life sciences by managing implementations, developing customer satisfaction strategies, and serving as a trusted advisor.

Define and execute the companys AI strategy; build and lead the data science, ML engineering, and AI teams; govern intake, prioritization, and AI guardrails; advance production ML, MLOps, LLMs and agentic workflows; partner cross-functionally to deliver measurable ROI; and report AI impact and roadmap to the CEO and board.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead zero-to-one transformation of Scribd's knowledge platform using ML/LLMs. Set product vision, roadmap, and success metrics; prioritize trade-offs; partner with Design, Engineering, ML, and Data Science; deliver incremental improvements and extensible platform capabilities; drive cross-functional alignment and measure core user journeys.

Lead statistical activities for clinical trials from protocol design through reporting. Provide statistical oversight, collaborate with cross-functional teams and CROs, apply SAS/R (and ML) for analyses, automate processes, and mentor junior statisticians.

The Lead Bioinformatics AI Scientist will lead AI and GenAI R&D initiatives to develop advanced clinical testing and genomic data analysis platforms, leveraging expertise in bioinformatics and machine learning.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

As a Senior Fullstack Engineer, you'll architect and build a high-concurrency auction engine, leveraging AI for enhanced user experiences and system reliability.

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.

Lead engineering strategy and delivery for customer-facing web and mobile platforms; drive AI-native transformation, increase conversion and speed-to-fund, raise engineering talent and velocity, ensure secure and compliant architectures, partner with product and cross-functional teams to deliver measurable business outcomes.

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.

The Senior Data Scientist will develop scalable ML models, analyze large datasets for insights, and collaborate with teams to enhance business performance through data-driven solutions.

The SVP of AI Innovation - Web Scraping will lead the development of AI-driven web scraping strategies, focusing on data acquisition, technology enablement, compliance, team building, and cross-functional collaboration for new data services.

The Product Manager will identify process improvements and innovations, utilize automation and AI, and manage the product lifecycle to enhance productivity and deliver business value.

Lead vision, strategy, and roadmap for pricing platforms, owning internal Rating Plan Manager and tooling to speed model deployment, improve workflow reliability and observability, enable self-service experimentation, and support regulatory/state-level pricing needs through cross-functional collaboration.

Lead complex enterprise fraud and financial-crime investigations using data analysis, predictive analytics and investigative tools. Design enterprise risk assessments, mentor staff, collaborate with HR/Legal/InfoSec, document findings, and recommend mitigation and prevention strategies.

Design and build reinforcement-learning training environments and diverse tasks to evaluate and improve LLM agents; iterate rapidly on task designs from customer feedback, deliver high-quality outputs with minimal supervision, and maintain PST overlap for collaboration.

The AI Application Engineer will design and deploy AI systems, collaborate with teams, implement Generative AI solutions, and promote continuous learning in AI practices.

Lead onboarding and training for new customers of an AI-powered construction SaaS. Customize training, run webinars and office hours, create documentation, drive user adoption, surface issues with support, and collaborate with product and customer success to refine onboarding processes.

Lead and operate a 10,000 sq ft micro-fulfillment lab to validate and scale warehouse automation, robotics, and AI-driven operations. Own strategy, budgeting, vendor selection, experimentation, and deployment frameworks to drive measurable productivity, capacity, accuracy, and cost improvements across Stord's fulfillment network. Build and mentor a multidisciplinary team and partner with internal and external stakeholders to commercialize solutions.

The Senior Engineering Manager at Planet leads the engineering team, focusing on delivering products for Defense Intelligence. Responsibilities include mentoring engineers, refining processes, and collaborating across teams for effective deployment of technology.

Lead strategy, roadmap, and delivery for Tekmetric's SaaS product for auto repair shops. Own user research, AI/ML product initiatives, cross-functional alignment, go-to-market coordination, and measurement of business outcomes. Train internal teams and iterate on features from concept through launch and optimization.