Top Remote Machine Learning Jobs in Salt Lake City, UT

Performs hands-on cybersecurity and AI engineering, defines and documents functional and technical requirements, converts requirements into Jira stories, validates application features, and collaborates with customers, developers, systems engineers, and AI stakeholders. Evaluates AI architectures, model performance, integration tradeoffs, and emerging AI/ML applications for enterprise software. Requires active TS/SCI with polygraph clearance, U.S. citizenship, a bachelor’s degree, and 15+ years of experience.

Own technical evaluations, deployments, and production integrations for major government and enterprise customers. Build Python, SQL, agents, tools, and REST integrations within customer environments; lead proofs of concept, deployments, executive briefings, and technical account relationships. Triage bespoke versus reusable capabilities, influence the product roadmap, and review other engineers’ work. The role requires strong cloud, containerization, data, communication, and ambiguous problem-solving skills.

Partner with Account Executives on discovery, demos, proof-of-value engagements, RFPs, and enterprise security reviews. Translate on-chain risk into business terms for executives and technical stakeholders, develop customized demonstrations, advise customers on integrations and compliance, and represent the company at industry events. Collaborate with Product, R&D, Security Engineering, and Customer Success to influence product direction, improve sales processes, and support customer onboarding and adoption.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Develop data-driven solutions to complex real-world problems using data science, machine learning, statistical modeling, and analytical reasoning. Candidates should currently work in a technical data science or machine learning role at a leading technology company, hold a quantitative undergraduate degree from a top-ranked university, and reside and work in an eligible English-speaking country.

Design and develop scalable backend systems using Kotlin, Micronaut, and Spring. Build RESTful APIs, optimize PostgreSQL and Elasticsearch databases, implement RabbitMQ-based asynchronous processing, integrate AI tools, and create automated tests. Deploy and monitor services on GCP using Docker, Kubernetes, and Terraform. Collaborate cross-functionally, participate in code reviews, troubleshoot performance issues, and deliver reliable backend products.

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.

Owns the strategy, requirements, evaluation, and quality of AI-powered products. Partners closely with ML, backend, and client engineering teams to define system behavior, assess model performance, manage trade-offs across quality, latency, cost, reliability, safety, and UX, and establish feedback loops. The role requires strong technical judgment, hands-on experience with AI or LLM products, and the ability to turn ambiguous problems into measurable requirements and executable plans.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead technical strategy and architecture for Azure Storage geo-replication, data protection, failover orchestration, and business continuity. Drive cross-team delivery, operational excellence, telemetry, automation, AI-assisted operations, and represent the team in executive and customer engagements.

Leads commercial analytics and business intelligence strategy, including executive dashboards, account reporting, revenue and pricing analytics, operational KPIs, forecasting, segmentation, and predictive modeling. Partners with Sales, Finance, Marketing, Operations, and Product to improve revenue, profitability, customer retention, and marketplace performance. Presents insights to executives and customers while managing analytics teams, budgets, vendors, and BI scalability.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Own the technical side of deal progression for airline deployments: qualify opportunities, run demos and POCs, author integration and deployment architectures (PSS, APIs, data feeds, DNS, identity, networking), respond to RFPs/security questionnaires, scope SOWs and NFRs, and build reusable demo/reference assets to drive adoption and expansion while liaising between sales, engineering, and customers.

Provides bioinformatics consultation, computational analysis, statistical support, and production pipeline development for HIV-related genomic and clinical research at NIAID. Responsibilities include sequence and gene expression analysis, variant calling, genome assembly, comparative genomics, proteomics, and epigenomic workflows. The role supports scientific programming, experimental design, data dissemination, scalable research resources, and publications. Candidates need expertise in programming, workflow frameworks, containerization, high-performance computing, and next-generation sequencing, with HIV research experience preferred.

Lead go-to-market strategy for Sprout Social’s AI portfolio, including the Trellis AI agent. Develop enterprise launch playbooks, positioning, executive messaging, campaigns, and enablement programs. Analyze market and competitive dynamics, influence product roadmaps, improve launch processes, and partner with Product, Sales, Revenue, Brand, and executive leadership to drive adoption, revenue growth, deal expansion, and category leadership. The role also mentors marketing team members and operates with substantial autonomy.

Leads technical direction for GitLab’s Core DevOps services, covering CI, CD, planning, and source control. Defines architecture, quality metrics, migration strategies, and technical roadmaps; addresses systemic risk and complex design decisions; prototypes and writes code when needed; partners with Infrastructure, Security, SRE, and Product; adopts AI capabilities; mentors senior engineers; supports incident response and senior technical hiring.

Own customer deployments of Sayari’s risk screening, graph analytics, investigation workflows, and data integrations. Responsibilities include technical scoping, production Python and SQL development, API integration, bulk data delivery, pipeline onboarding, dashboard creation, monitoring, troubleshooting, documentation, and feeding recurring customer needs back to engineering. The role requires client-facing communication, independent problem-solving, and collaboration across multiple customer accounts, with 25–30% travel and in-office time.

Designs and maintains automated, AI-enabled detection and response capabilities across enterprise security platforms. Integrates SIEM, EDR, SOAR, and threat intelligence technologies; analyzes security telemetry; develops and validates detection content and automation workflows; documents procedures and runbooks; and collaborates with SOC, threat intelligence, and incident response teams. The role also evaluates emerging security capabilities and supports continuous improvement of detection operations for a federal agency.

Paid, part-time Creative Advisor collaborating with AI research and machine learning teams. The role involves sharing creative expertise, defining quality standards, developing work, reviewing model outputs, identifying limitations, and helping fellow creators engage with the program. Applicants should be leading creators with strong portfolios and interest in shaping AI’s impact on creative practice; no AI experience is required.

Applies statistics, experimentation, data analysis, and machine learning to complex datasets and AI model evaluation. Designs analyses, develops and validates models, performs Python and Pandas data transformations, creates expert prompts and datasets, identifies quantitative errors, and provides structured feedback. Produces reproducible code, analytical reports, and clear documentation while collaborating with stakeholders and reviewing peer contributions.

Defines enterprise technology architecture, modernization roadmaps, governance standards, and cloud, infrastructure, networking, security, resilience, and AI strategies. Designs Azure, AWS, and hybrid environments; guides migrations, automation, identity, connectivity, disaster recovery, and cost optimization. Leads architecture reviews, evaluates vendors and emerging technologies, translates regulatory requirements into controls, and influences executives and cross-functional technical teams through implementation.

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.

Leads enterprise AI platform strategy, architecture, engineering, governance, and operations across machine learning, generative AI, agents, data, and business intelligence. Owns platform standards, reusable services, lifecycle management, security, compliance, reliability, vendor commitments, budgets, and consumption. Directs production-scale AI enablement, establishes responsible AI controls, and manages engineering teams, staffing, operating plans, and executive stakeholder alignment.

Own strategy and execution for integrated B2B marketing campaigns that drive enterprise pipeline, self-service growth, platform adoption, and category awareness. Coordinate ABM, content, paid media, events, lifecycle, social, partnerships, PR, and sales activation. Manage launches, optimize audiences and conversion paths, orchestrate teams and agencies, and measure impact across engagement, pipeline, acquisition, product adoption, and awareness.

Own ElevenLabs’ North American enterprise financial services sales motion across banking, insurance, lending, payments, and wealth. Define the ideal customer profile, sales methodology, pipeline and forecasting processes, land major enterprise accounts, and expand deployments. Build and lead the sales team, guide regulated buyers through risk, compliance, model-risk, architecture, and security reviews, and manage executive negotiations. Partner with engineering, product, marketing, and customer success to develop repeatable conversational AI sales strategies.