Top Machine Learning Jobs in Salt Lake City, UT

Build and deploy production AI agents, agentic workflows, and ML-enabled enterprise features within Java/Spring microservices. Implement evaluation, monitoring, guardrails, safety, testing, and observability for LLM applications. Collaborate with AI scientists, engineers, product managers, and technical specialists to productionize models, integrate RAG and tool calling, prototype solutions, and improve AI capabilities. The role also involves design reviews, agile planning, software quality ownership, and staying current with emerging AI technologies.

Own end-to-end analytics delivery for a client POD, translating business priorities into workplans, coordinating cross-functional teams, governing delivery and revenue KPIs, and managing risks through resolution. Perform hands-on data profiling, validation, reconciliation, exploratory analysis, automation, and quality reviews using SQL, Python, Databricks, and Excel. Create executive-ready KPI, revenue, and business-impact narratives, prioritize initiatives, support business cases, and identify opportunities for analytics automation and AI-enabled improvements.

Lead the architecture and development of Root’s machine learning platform for insurance pricing. Build infrastructure for feature pipelines and stores, reproducible training and orchestration, model registries, serving, validation, observability, and research-to-production workflows. Establish versioned contracts and technical standards, automate workflows using LLMs, write and review critical code, influence cross-team strategy, and mentor senior engineers. Collaborate closely with data scientists and researchers to create scalable, reliable, and reproducible ML capabilities.

Lead the design and implementation of large-scale AI and machine learning systems for Yahoo Search across multiple teams. Define data strategies, evaluation frameworks, and scalable production-serving approaches. Drive technical alignment among engineering, product, design, and research teams; guide adoption of large language models, vision models, generative AI, and agentic AI; and oversee data analysis informing strategic product decisions.

Build production machine learning benchmarks and evaluation systems for foundation models. Own backend infrastructure including data pipelines, execution environments, storage, and orchestration, as well as sandboxed environments for agentic evaluations. Partner with researchers, enterprise customers, and company leadership to scope and deliver technical solutions. Identify scalable evaluation patterns, communicate clearly, and operate effectively in an ambiguous, high-ownership startup environment.

Build and operate scalable ML infrastructure and platform capabilities across cloud and on-premises environments. Develop Python-based developer tooling, services, and production systems supporting experimentation, distributed training, model deployment, and operations. Lead complex initiatives from architecture through rollout, improve reliability and developer productivity, and partner with ML and infrastructure teams. The role emphasizes Kubernetes, AWS, distributed compute, GPU-intensive workloads, observability, and production excellence.

You will tackle AI challenges, develop scalable software and collaborate with teams to enhance healthcare navigation using AI solutions.

Lead and transform the Fraud Intelligence Team into a high-impact function by improving operating models, QA, tooling, and metrics; build entry-level talent programs; connect labeling to measurable fraud prevention outcomes; deliver actionable insights to Product, R&D, Analytics, and GTM; and leverage AI and automation to increase productivity and business impact.

Partner with functional leaders to translate business strategy into people strategy. Own org design, performance management, compensation, talent development, engagement, and retention. Provide coaching on career pathing, change management, and sensitive employee issues. Use people data to generate insights and recommendations, build compliant HR policies, manage global employee relations, and collaborate with People Team COEs to deliver cohesive people programs at scale.

Serve as second-in-command for Coinbase Derivatives' Market Regulation Department, leading day-to-day regulatory compliance, managing exams and audits, advising on CFTC Core Principles, reviewing surveillance escalations and enforcement actions, and coordinating cross-functional regulatory workstreams to ensure the DCM meets obligations and resolves findings timely.

Lead a team to design, develop, test, implement, and optimize AML transaction monitoring scenarios. Manage the full scenario lifecycle, collaborate with stakeholders (Product, Technology, Decision Science, Model Risk), evaluate ML opportunities, govern project intake, and report on scenario performance and KRIs/KPIs. Continuously improve detection coverage, efficiency, and documentation to support SAR processes.

Develop and maintain open-source Python frameworks and ML developer tools, architect scalable libraries, grow early-stage projects, build real-time streaming capabilities, integrate modern frontend technologies, and collaborate with open-source contributors and the broader developer community.

Drives operational excellence through data analysis, dashboards, process improvements, and cross-functional project leadership. The role embeds with business units to manage day-to-day operations, investigate trends, answer strategic questions, implement workflows, coordinate stakeholders, track deliverables, and present insights to technical and non-technical audiences. It serves as a strategic partner to operations leadership in a fast-paced, ambiguous environment.

Develop experimental AI techniques for agentic marketing applications, especially image and video generation. Build proofs of concept, autonomous evaluation and improvement systems, realistic AI-generated video, and brand-aligned creative generation workflows. The role requires strong quantitative and probabilistic thinking, backend architecture skills, creativity with LLM applications, and product intuition.

Lead development and maintenance of character creation, rigging, animation, and simulation tools for game production. Collaborate with artists, engineers, studios, and research teams to prototype workflows, integrate game engines, optimize environments, and apply AI/ML to improve development efficiency. Build and customize internal tools, automate project environments, troubleshoot production issues, document systems, and support varied client pipelines. Participate in internal demonstrations and conferences, with required travel.

Define and execute the market strategy for UserTesting’s AI solutions. Own positioning, messaging, value propositions, go-to-market plans, sales enablement, customer and buyer research, competitive differentiation, thought leadership, and market feedback loops. Translate complex AI workflows and human-judgment applications into compelling solutions for technical enterprise buyers, while influencing product and company strategy through cross-functional leadership.

Manage strategic enterprise accounts across a national territory, expanding existing customers and acquiring new logos. Build consultative account strategies, serve as a trusted AI and customer experience advisor, develop customer and channel-partner relationships, forecast and exceed bookings targets, and coordinate cross-functional teams to maximize customer and business outcomes. Support initiatives across the customer lifecycle, including business development, messaging, commercial positioning, and operational effectiveness.

Seller-doer solution architect who leads technical discovery, architects multi-vendor data and AI platforms, builds proposals and carries quota, then stays hands-on to guide delivery, write production code, and expand accounts across Snowflake, Databricks, cloud, and modern data tooling.

Leads product strategy, roadmap development, prioritization, product metrics, and go-to-market execution for a marketing planning and analytics platform. Manages and coaches product managers while acting as an individual contributor on key initiatives. Partners with the CEO, engineering, sales, marketing, and leadership teams to deliver API-based, data-heavy statistical and machine learning products for technical B2B SaaS customers.

Owns the strategy, roadmap, development, and adoption of financial data products and APIs. Partners with data science and engineering to integrate predictive and machine-learning models into scalable, trustworthy capabilities. Defines quality metrics, requirements, documentation, and support models while ensuring privacy, security, regulatory compliance, and downstream adoption across consumer and business banking products.

Drive MSSP partner growth across AMER by recruiting, onboarding, activating, and expanding strategic managed security service provider relationships. Develop territory recruitment plans, executive partnerships, joint business plans, and indirect routes to market. Help partners launch managed security services, generate pipeline, increase revenue, and accelerate time-to-revenue. Collaborate with sales, channel, marketing, services, and technical teams while managing forecasts, opportunities, executive reviews, and revenue objectives.

Develop and deploy mineral exploration instruments, sensor systems, data acquisition methods, and proprietary software. Integrate geospatial, geophysical, geochemical, geological, and geographic data; build statistically rigorous and physics-based models; identify ore-forming anomalies; and generate 2D and 3D exploration predictions. Collaborate with data scientists, engineers, geoscientists, vendors, and external partners to design field programs, deploy sensors, reduce uncertainty, and guide exploration decisions.

The Machine Learning Engineer will build data-intensive products, deploy ML models, analyze datasets, and collaborate with product teams to enhance Nextdoor's platform.

Lead AI-driven localization product initiatives from concept through production. Build strategic roadmaps, incubate machine-learning capabilities, partner with researchers and engineers, and deliver scalable tools for creators and content operations. Translate user needs into impactful products, communicate strategy to executives and nontechnical stakeholders, and use data, experimentation, and ML evaluation to prioritize work and measure outcomes.

Architect and operate North’s AWS and multi-cloud infrastructure, establishing IaC, CI/CD, security, observability, and disaster-recovery standards. Build AIOps capabilities including anomaly detection, predictive scaling, self-healing remediation, AI-assisted tooling, and cloud cost optimization. Collaborate with DevOps, SRE, Product Engineering, and InfoSec teams to deliver resilient, compliant, highly scalable platforms.