Top Machine Learning Jobs in Salt Lake City, UT

Design and implement scalable analytics/data models and dbt pipelines, manage Databricks, build dashboards and BI solutions, partner with business stakeholders, support CRM/marketing analytics, and explore GenAI/ML opportunities to deliver reliable, automated data products.

Founding Product Manager responsible for defining and launching Hightouch’s Agentic Personalization product. The role includes customer discovery, product strategy, agent workflow and approval design, roadmap sequencing, technical decision-making, revenue alignment, competitive positioning, customer calls, and go-to-market collaboration. The PM will work cross-functionally with engineering, design, sales, customer success, founders, and executives to establish product-market fit and scale a new enterprise SaaS offering.

Leads Hightouch’s machine learning organization, including product development, technical roadmapping, recruiting, reliability, and people management. The role focuses on personalization, product recommendations, probabilistic identity resolution, LLM-powered content generation, and scalable machine learning infrastructure for model training and inference.

Develop applied AI research prototypes and systems for agentic marketing applications, focusing on generative image and video, brand asset creation, data workflows, and autonomous evaluation. The role combines probabilistic and quantitative reasoning, backend system architecture, product intuition, experimentation, and customer-focused product development. Responsibilities include building proofs of concept, improving AI-generated media realism and brand alignment, and architecting agentic systems.

Design and launch 0-to-1 AI-powered marketing tools for Hightouch’s Agents product suite. Responsibilities include rapid prototyping, customer research, synthesizing usage data and feedback, shaping product strategy, and collaborating with designers, engineers, and product managers. The role requires strong visual, UI, and interaction design skills, comfort with ambiguity, curiosity about AI, and rapid iteration in a remote-first environment.

Owns multistate U.S. property and casualty insurance product filings from drafting through approval, including AI model documentation, SERFF submissions, objection responses, and regulatory inquiries. The role supports carrier clients, monitors state requirements, develops scalable filing processes, and coordinates with data science, actuarial, product, counsel, regulators, and industry stakeholders. Requires strong technical writing, insurance regulatory expertise, independent judgment, and the ability to translate complex predictive modeling methods into regulator-acceptable documentation.

Supports Woolpert’s strategic Google partnership by coordinating joint go-to-market initiatives, integrated campaigns, events, webinars, customer stories, and digital programs. Develops partner sales enablement materials and messaging for complex digital capabilities, builds relationships across Google and internal teams, supports co-selling opportunities, and tracks campaign performance, pipeline influence, engagement, and marketing ROI. The role requires cross-functional collaboration with marketing, sales, technical, and executive stakeholders, with travel up to 25%.

Manage complex customer issues, triage support activity, and coordinate escalations across Client Engagement, Product, Engineering, and project teams. Analyze tickets and feedback to identify trends, risks, process gaps, and product opportunities. Improve support operations through documentation, dashboards, workflows, routing logic, playbooks, and feedback loops. Serve as a customer advocate while supporting strategic initiatives and strengthening handoffs, prioritization, and operational decision-making.

Lead data engineering and machine learning initiatives by designing, architecting, developing, and supporting GCP data pipelines. Create information architecture documentation, estimate and plan technical work, collaborate with project teams and business analysts, and translate requirements into technical designs. Develop Python-based ELT pipelines, optimize BigQuery SQL, support CI/CD, and provide technical leadership to projects and teams.

Build analytical frameworks, measurement methodologies, and decision support to drive enterprise decisions across marketing, loyalty, pricing, menu, media, and operations. Mine and integrate customer, transaction, digital, and operational data; develop segmentation, predictive models, and dashboards; lead test-and-learn, incrementality, and forecasting efforts; translate analysis into clear business recommendations and scalable decisioning tools.

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.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

Develop deep learning and AI/ML models to predict cardiac events from ECG and physiological signals. Apply signal processing, analyze implantable-device datasets, build Python tools, validate algorithms, and support feasibility studies for diagnostic features. Collaborate with clinical, software, and hardware teams to improve implantable-device performance and patient outcomes.

Generates and qualifies leads through outbound outreach, manages sales pipelines and CRM activity, conducts product demonstrations, guides prospects through the sales process, builds customer relationships, meets quotas, and collaborates with marketing, product, and customer success teams. The role requires inside sales experience, SaaS or technology familiarity, strong communication and negotiation skills, CRM proficiency, and up to 20% travel.

Design and operate large-scale, AI-native cloud infrastructure (especially Kubernetes/AKS). Improve service quality, automate incident prevention/remediation, accelerate engineering productivity, and integrate AI tools into development workflows.

Delivers customer-facing training in AI, data, and software engineering; creates tailored courses, projects, certifications, and assessments on the DataCamp platform. Partners with Customer Success, Sales, Product, and Content teams to design blended learning paths, teach virtual and onsite sessions, tailor materials for varied audiences, and improve customer adoption, satisfaction, and measurable skills.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Design, build, and operate high-throughput, low-latency ad-serving infrastructure processing millions of requests per second. Optimize GPU-powered TensorRT inference, feature stores, model serving, benchmarking, experimentation tooling, and system reliability. Partner with machine learning engineers to productionize models and own systems through design, deployment, observability, capacity planning, incident response, and optimization.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Leads Clipboard’s Applied Science function and team, setting the science roadmap across marketplace pricing, auctions, causal models, metrics, and experimentation. The manager balances production modeling with analytical work, partners with engineering, product, finance, and operations, and mentors and hires scientists. Responsibilities include developing and deploying quantitative models, designing experiments, defining marketplace metrics, prioritizing initiatives, and communicating findings to business and technical stakeholders.

Build and improve decision systems, predictive models, experiments, and advanced analytics solutions. Explore data sources, prototype technology-driven solutions, evaluate algorithms, and recommend strategic business changes through rigorous analysis. The role requires applying machine learning and software engineering practices to real-world product, customer, and financial problems.

Develop and validate machine learning, deep learning, and signal-processing algorithms using large-scale ECG and longitudinal health datasets. Build scalable machine learning infrastructure, collaborate with data scientists, software engineers, and clinical experts, and translate research into production-quality medical device algorithms. Communicate findings through publications, conference presentations, and stakeholder briefings. Work includes distributed model training, cloud deployment, time-series analysis, and advanced methods such as self-supervised learning and generative AI.

Leads the engineering team responsible for Airbnb’s globally scalable AirCover insurance platform. Sets technical strategy, architecture, roadmap, and operational standards for configurable insurance products, pricing, enrollment, coverage management, and experimentation. Partners with Product, Data Science, Legal, Insurance, Finance, and Operations to expand across regions and improve attach rates. Manages hiring, mentoring, career development, incident response, and engineering quality while guiding distributed, high-availability backend systems and ML-driven experimentation.

Leads advanced analytics initiatives supporting Tribal healthcare programs and strategic decision-making. Develops predictive and prescriptive models, integrates complex data sources, establishes data quality frameworks, evaluates healthcare data resources, and produces reports and dashboards. Coordinates cross-functional projects, ensures federal compliance, supports quality measurement, mentors junior staff, and may serve as a team lead.

Leads complex enterprise customer engagements as a technical authority for Databricks data and AI solutions. Drives architecture, platform adoption, consumption strategy, competitive positioning, and executive relationships. Designs large-scale data, streaming, migration, governance, and AI/ML solutions; orchestrates cross-functional teams; mentors other solutions architects; and contributes to technical thought leadership. Requires advanced coding, enterprise data architecture expertise, Databricks proficiency, strategic account leadership, and executive communication.