Top Senior Machine Learning Jobs in Salt Lake City, UT

Serve as the persistent technical partner for strategic and premium customers, diagnosing workflows, labeling, configurations, model behavior, and technical health. Build success plans with Customer Success Managers, guide implementations and model releases, conduct technical health reviews, coordinate fraud incident responses, and communicate technical findings to business and engineering stakeholders. Identify recurring customer patterns and provide feedback to Product and Engineering while supporting account retention and growth.

Own technical relationships with insurance carrier accounts by leading discovery, solution design, integrations, deployment support, defect management, QA, documentation, API and payload validation, SQL-based account monitoring, and incident management. The role partners with client engagement and engineering teams to ensure deployments meet carrier requirements and progressively assumes greater ownership of technical account relationships.

Lead and grow a team of Conversation Designers to build and scale conversational AI experiences. Partner with Product, Engineering, Analytics, and ML teams to drive strategy, tooling, evaluation, and quality practices. Recruit and remove roadblocks, own strategic initiatives, and remain hands-on in designing, testing, and improving AI-driven conversational products.

Leads strategy, roadmap, execution, commercialization, and customer outcomes for a major enterprise software product area. Conducts market and customer discovery, prioritizes opportunities, partners with engineering and design, establishes outcome-based metrics, and supports sales enablement. Builds and coaches the product management team, manages executive and customer relationships, and helps transition the company from services-led to product-led operations. The role emphasizes legal technology, eDiscovery, AI-enabled workflows, enterprise data, and regulated-industry requirements.

Develop machine learning, AI, statistical, and predictive models using Snowflake and other data sources. Perform data extraction, cleansing, feature engineering, experimentation, visualization, and quality analysis. Partner with business and technical teams to deliver data products, conversational analytics, and actionable recommendations. Lead discovery sessions, communicate insights to nontechnical audiences, mentor analysts, and support enterprise data science projects.

Own technical delivery and account health for insurer software deployments. Responsibilities include discovery, solution design, integrations, API and payload review, QA testing, defect documentation, workflow mapping, SQL-based production monitoring, incident management, and technical communication with carrier accounts and engineering teams.

Lead hands-on engineering for an AI-powered email application across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Define APIs and abstractions, make architecture and scalability decisions, improve observability and reliability, and lead a small team of senior engineers through technical reviews, mentorship, and production delivery.

Lead Runpod’s Americas sales organization and revenue number across new business, expansion, and renewals. Manage and coach Account Executives and Account Managers, oversee forecasting and pipeline, lead complex enterprise deals and contract negotiations, build regional go-to-market strategy and sales playbooks, and convert product-led demand into revenue. Partner with Marketing, Product, Legal, and Finance while improving sales efficiency through AI and developing a remote-first team.

Serves as a permitting and plan review domain expert for engineering, product, design, data, and go-to-market teams. Reviews permit applications, plan sets, submittals, and AI outputs; interprets building codes; identifies missing or noncompliant information; defines approval and revision criteria; labels examples for ML systems; synthesizes jurisdictional requirements; and develops rubrics, checklists, documentation, and benchmarks. Requires approximately 5+ years of hands-on permitting or plan review experience and fluency in applicable building codes.

Leads the Product Acquisition engineering team and drives technology strategy, architecture, software delivery, DevOps maturity, platform adoption, vendor management, and operational excellence. Partners with product managers on roadmaps and business outcomes, ensures engineering standards and production support, and coaches, recruits, and develops engineers and consultants. Requires strong software development, product operating model, project management, stakeholder communication, and technical leadership experience.

Conduct research on frontier AI systems and develop datasets, experiments, prototypes, tooling, evaluation frameworks, and reinforcement learning environments. Investigate synthetic and agentic data generation, post-training, model understanding, benchmarks, and evaluation. Collaborate with research, engineering, product, and operations teams to translate findings into scalable AI applications, while contributing to technical publications, open-source work, mentorship, and the broader AI research community.

Conduct original research to evaluate, train, and improve frontier AI systems. Responsibilities include developing STEM benchmarks, synthetic data methods, reliability and hallucination evaluations, verification techniques, and agentic science workflows. The role involves identifying research gaps, designing rigorous experiments, building evaluation pipelines, publishing findings, and proposing new research directions across reasoning, AI evaluation, data generation, and AI-for-science.

Conduct frontier AI research and build research-grade datasets, prototypes, tooling, reinforcement learning environments, benchmarks, and evaluation frameworks. Investigate synthetic and agentic data generation, post-training, model understanding, and model capabilities. Design rigorous experiments, analyze results, and translate findings into scalable products and AI systems while collaborating across research, engineering, product, and operations teams.

Lead the development and production deployment of applied machine learning and LLM solutions across product, marketing, and operations. Own ranking, recommendation, filtering, personalization, experimentation, and model evaluation systems. Partner with business and technical teams to identify valuable use cases, establish modeling best practices, and ensure reliable production deployment. Manage and mentor applied ML practitioners while maintaining technical rigor, business alignment, and measurable customer and revenue impact.

Lead the design, development, deployment, and operation of production AI and ML systems. Build high-throughput Python services and Databricks pipelines, support traditional ML and LLM applications, establish testing and deployment standards, and develop monitoring for quality, reliability, latency, throughput, and cost. Partner with product, research, and responsible AI teams on governance and release criteria. Provide architectural leadership, conduct code reviews, mentor engineers, and resolve complex technical dependencies.

Leads and builds the mid-market business development function, owning territory strategy and the full sales cycle for pharma and biotech accounts. Develops and coaches a high-performing BD team, drives pipeline and new-logo acquisition, closes multi-year SaaS deals, and partners with marketing, strategy, product, and commercial operations. Represents the company at industry events and contributes to commercial frameworks, forecasting, segmentation, and coverage models.

Lead technical product strategy and execution for AI-powered learning intelligence infrastructure. Own roadmaps for AI curation, data ingestion, recommendation algorithms, learner profiles, analytics, and platform interoperability. Translate architecture, research, data, and business requirements into technical specifications, backlogs, success criteria, and implementation plans. Coordinate cross-functional stakeholders, manage delivery risks, and ensure systems are explainable, privacy-aware, validated, and aligned with educator and learner needs.

Manage a portfolio of B2B SaaS customers in the HR technology space, driving renewals, expansions, and revenue growth. Build strategic client relationships, conduct business reviews, monitor customer health, manage renewal and sales cycles, introduce products, and advocate for customers internally. The role requires strong sales acumen, analytical abilities, HR technology expertise, and experience influencing HR and L&D decision-makers. It also includes identifying adoption barriers, creating customer feedback loops, and mentoring others.

Leads an enterprise Account Executive team across a U.S. region, owning pipeline, revenue, forecasting, account planning, sales processes, onboarding, coaching, and growth targets. Supports strategic customer deals, partners with marketing on account-based campaigns, builds utility-industry relationships, and represents Neara at customer sites and industry events. The role also develops user groups and customer feedback loops to inform product direction.

Leads enterprise product evaluations, implementations, integrations, and adoption initiatives. Manages cross-functional projects involving AI, automation, system modernization, and third-party platforms. Translates business requirements into implementation plans, develops business cases and ROI analyses, coordinates vendors and stakeholders, prepares executive communications, assesses risks, and supports organizational change and user adoption.

Owns the intelligence platform roadmap for enterprise healthcare engagement, including decisioning, orchestration, integrations, interoperability, identity and consent management, and scalable deployment. The role defines strategy and requirements, prioritizes platform investments, leads partner integrations, establishes success metrics, and coordinates Engineering, AI/ML, Data Science, Security, Design, and client teams. It also embeds healthcare compliance, security, and responsible AI throughout the product lifecycle.

Provides strategic finance leadership to venture-backed AI and SaaS startups. Responsibilities include monthly close oversight, budgeting and forecasting, capital planning, treasury management, board reporting, investor updates, fundraising, M&A support, cap table analysis, revenue cycle management, cash flow planning, controls, audit readiness, and advising founders on financial strategy.

Build edge-focused full-stack product experiences integrating machine learning and autonomy for frontline defense users. Collaborate with designers, infrastructure teams, product teams, and ML engineers to create intuitive applications, efficient edge-deployed application stacks, and internal modeling APIs. Develop maintainable software using strong engineering practices, including design documents, code reviews, and style guides.

Design, build, deploy, and optimize scalable AI and machine learning systems using large datasets. Develop predictive models, recommendation systems, personalization engines, forecasting solutions, generative AI applications, and production ML pipelines. Collaborate with data, backend, and product teams to integrate models into applications, while monitoring performance, data quality, drift, reliability, and cost. Establish best practices for evaluation, experimentation, reproducibility, and deployment, and research emerging AI technologies.

Leads enterprise product and technology integrations, cross-functional projects, AI and automation initiatives, business analysis, vendor coordination, executive communications, and change adoption. Develops requirements, implementation plans, business cases, risk strategies, and executive reporting while ensuring initiatives meet scope, schedule, compliance, and organizational objectives.