Top Machine Learning Jobs in Salt Lake City, UT

Leads a span of approximately 20 engineers while shaping hiring, onboarding, career development, staffing, and engineering enablement across the AI practice. The role is also approximately 50% billable client delivery, providing executive advisory on AI software development lifecycles, governance, agentic deployments, MLOps and LLMOps pipelines, cloud platforms, observability, and production resilience. Requires extensive engineering leadership, organizational scaling, consulting, GCP, and production AI/ML deployment experience.

Build and operate scalable cloud data pipelines, warehouses, transformation layers, and ML production systems. Lead data infrastructure architecture, warehouse performance optimization, DataOps monitoring, CI/CD improvements, data quality testing, and large-scale data product delivery. Collaborate across teams, promote data stewardship, and support batch and streaming pipelines while deploying, serving, and monitoring machine learning models.

Build backend services, APIs, data-processing systems, and frontend features for Northbeam’s marketing intelligence platform. Work on experimentation, near-real-time data infrastructure, AI-powered workflows, data integrations, RBAC, and end-to-end product delivery. Collaborate with data scientists, product managers, designers, and senior engineers while contributing across backend, frontend, pipelines, and infrastructure in a fast-moving environment.

Leads and builds Instructure’s decision science function, including hiring, team development, roadmap ownership, experimentation, causal and predictive modeling, executive insights, and data literacy. Partners with product, engineering, marketing, finance, and executives to guide product, pricing, retention, go-to-market, and operational decisions. Establishes analytical standards, supports data infrastructure, and ensures ethical, privacy-conscious use of student data.

Leads end-to-end data architecture, engineering pipelines, semantic-layer development, data quality, monitoring, and platform operations. Manages agile sprint planning, release delivery, DevOps metrics, and cross-functional project plans. Establishes MLOps and machine-learning platform capabilities, mentors data engineers, manages team members, and ensures reliable, well-governed data solutions for business intelligence and reporting.

Leads end-to-end data architecture, engineering pipelines, ETL, data modeling, quality monitoring, and Tableau BI platform operations. Partners with stakeholders to define requirements, manage project plans, lead agile sprints, and deliver reliable data solutions. Establishes governance, security, performance, and continuous improvement practices while developing AI-enabled analytics capabilities. Mentors data engineers and manages team performance, documentation, dependencies, and delivery across multiple workstreams.

Lead and coach the engineering team building AutoReview, an AI-powered mechanical design review product. Partner with Product on roadmap planning, feasibility, estimation, prioritization, and timelines. Maintain technical context to identify risks and dependencies, guide initiatives involving DFM checks, assembly analysis, and bulk workflows, and collaborate with applied ML teams. Establish clear ownership, support engineer autonomy, and help the team navigate evolving customer needs and AI technologies.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

Designs, develops, tests, deploys, maintains, and enhances machine learning and AI solutions for healthcare. Responsibilities include managing ML codebases and pipelines, collaborating with clinical and research teams, implementing scalable workflows on HPC resources, reviewing code, troubleshooting models and data analysis, supporting publications, and developing reliable production services. The role also involves technical leadership, system debugging, on-call support, and cross-functional collaboration within the Innovation Office.

Leads advanced data science initiatives across clinical and operational healthcare domains. Develops and deploys machine learning and statistical models, establishes scalable data and engineering standards, manages experimentation and model monitoring, and ensures reproducible analysis. Works with Python or R and SQL to process healthcare data, communicates findings to technical and executive stakeholders, mentors data professionals, and follows HIPAA, PHI, ethical AI, and data governance requirements.

Lead and develop underwriting supervisors and team leads to ensure consistent, high-quality SMB loan decisioning. Set operational strategy, manage capacity and workforce planning, drive metrics and cross-functional initiatives, serve as escalation for complex decisions, and partner with Credit, Risk, Compliance, and Product to improve policies and processes.

Lead the technical direction for scalable, cloud-native, API-first products and internal platforms. Design, build, test, deploy, monitor, and optimize distributed systems; contribute to conversational AI capabilities; improve CI/CD and engineering workflows; resolve performance issues; and provide technical leadership across architecture, coding standards, project execution, and team collaboration.

Lead Amazon performance marketing strategy and execution, manage Sponsored Ads and DSP, collaborate with agencies and cross-functional teams, optimize campaigns and budgets, analyze performance with AMC/analytics, and measure commercial impact to drive ROAS and customer acquisition.

Owns US customer and commercial operations for a healthcare hardware and AI company. Responsibilities include forecasting camera demand, managing manufacturing partners and inventory, launching repair and replacement services, executing and negotiating customer contracts, managing legacy obligations, creating SOPs and policies, and automating operational workflows with AI tooling. The role is remote, US-based, with occasional travel for customer engagements and team offsites.

Own adoption, retention, and expansion across a pooled portfolio of scaled customers through digital-first programs. Provide technical API integration guidance, troubleshoot issues, run demos, and create reusable enablement assets. Design automated customer journeys, webinars, self-service resources, and workflows that accelerate activation. Monitor usage and health data, identify expansion opportunities, report KPIs, and build scalable systems that reduce recurring manual work.

The Software QA Engineer will design and execute testing across web, mobile, desktop, API, hardware, and AI-powered products. Responsibilities include test planning, functional and regression testing, API validation, troubleshooting client-server issues, defect management, release readiness, and connected-device qualification. The role also evaluates AI output quality, reliability, bias, hallucinations, and safety. Collaboration with Product, Engineering, Design, Data Science, and Program teams is required in an in-office Agile environment.

Lead CX data intelligence initiatives using SQL, dashboards, analytical models, and AI/LLM techniques. Analyze customer journeys, operational metrics, churn risks, and retention indicators; develop insight agents and self-service reporting; define CX health metrics; and translate findings into strategic recommendations, process improvements, and experiments. Partner with operations, support, product, data, and engineering teams while maintaining data quality, governance, and documentation.

Design, train, and deploy scalable ML models (generative, classification, regression). Define feature roadmaps, collaborate with engineering to implement code and APIs, adapt ML methods for distributed/GPU environments, and create evaluation/annotation programs for fine-tuning and performance measurement.

Design, build, and deploy autonomous AI agents using LLMs and agent frameworks; integrate agents into e-commerce and supply chain systems; implement multi-agent orchestration, memory and tool integrations; fine-tune models and apply RL concepts; and build safety, monitoring, and MLOps pipelines.

Leads responsible AI and model governance while building and deploying healthcare fraud, waste, and abuse models. Establishes standards for documentation, monitoring, bias, drift, validation, and production readiness. Evaluates generative AI use cases, develops policies, validates large-scale claims models, and presents methodology and controls to partners, auditors, and investigators. Works remotely with data scientists, BI developers, and healthcare subject-matter experts.

Own and execute multi-year strategic partnerships with a hyperscaler and an AI lab, aligning technical integrations, commercial motions, and joint go-to-market plans. Coordinate cross-functional teams, run operational cadences, track measurable outcomes (pipeline, ARR, integrations), and represent partnerships to executives while driving hands-on execution and follow-through.

Build and operate production search, ranking, recommendation, and multimodal AI systems for a global jewellery marketplace. Own machine-learning models end to end, including feature development, training, evaluation, deployment, monitoring, and low-latency serving. Develop contextual and conversational search, image-based retrieval, learning-to-rank systems, and embedding pipelines. Define ranking experiments, evaluate LLM behavior, and design graceful recovery from probabilistic failures across multi-tenant storefronts.

Build and maintain Safari AI’s cloud infrastructure, including production services, system design, reliability, documentation, and security compliance. The role sets technical direction for the cloud stack, collaborates with engineering and operations teams, improves development processes, and delivers incremental features. Candidates should have 4–6 years of software engineering experience, public cloud expertise, and experience taking complex services from concept through production. Streaming processing and machine learning experience are advantageous.

Design, develop, implement, support, and improve financial-system integrations, databases, reporting solutions, and automated processes. Lead technical financial systems projects from requirements gathering through implementation, focusing on Aderant Expert, SQL Server, SSIS, SSRS, and T-SQL. Collaborate with Finance, IS, vendors, and business stakeholders; troubleshoot complex data issues, optimize database performance, document technical solutions, and support multiple concurrent initiatives.

Conduct independent research on frontier AI evaluation and auditing. Design rigorous evaluations, audit methods, and reproducible experimental pipelines; analyze model capabilities, risks, robustness, and alignment-relevant behavior. Produce audit reports, recommendations, publications, tools, and presentations while collaborating with technical, policy, engineering, and standards teams. The role requires careful handling of confidential information and may involve pilot audits with frontier AI companies.