Top Remote Machine Learning Jobs in Salt Lake City, UT

Build and validate production-grade ML and AI systems across pricing, underwriting, claims, and portfolio management. Work with structured and unstructured data, LLMs and agentic workflows, extract information from documents, resolve entities, create feature pipelines and inference services, quantify uncertainty, monitor drift, and measure business impact in collaboration with engineers, actuaries, underwriters, and product teams.

Own end-to-end production ML systems for search, recommendations, and ranking: architecture, training pipelines, inference, deployment, monitoring, drift detection, and retraining. Partner with data, product, and backend teams to ship reliable, low-latency ML-driven product experiences and set standards for ML infrastructure and model health.

Lead and grow a team of ML engineers building production ML systems for Fetch's Ad Platform. Translate product strategy into roadmaps, guide design and implementation of models for ranking, targeting, bidding and forecasting, set metrics and experimentation, enforce architectural and operational standards, manage trade-offs across performance and reliability, and drive cross-functional alignment to improve advertiser and member outcomes.

Lead and grow a distributed ML engineering team that runs experiments, ships production models, and partners with Risk and platform teams. Balance research bets and delivery, enforce rigorous evaluation and reproducibility, mentor engineers, own roadmap trade-offs, and represent results to stakeholders while improving model performance and production reliability.

Lead architecture and technical strategy for large Azure modernization and AI programs. Define reference architectures, drive cross-team execution, promote AI-augmented practices, mentor architects, guide discovery and delivery, ensure non-functional requirements (security, reliability, observability) are met, and create reusable assets to scale enterprise AI/ML adoption and platform modernization.

Design and train foundation vision-language models that read entire CT studies end-to-end. Build custom PyTorch architectures and distributed training pipelines, run large-scale experiments, evaluate models for clinical safety, and move models through FDA submission to hospital deployment.

Build and maintain backend services and infrastructure for ML and generative AI features. Develop scalable, resilient, high-performance Python services (async frameworks, streaming), deploy to Kubernetes, integrate RAG and LLM orchestration, instrument LLM observability, and collaborate cross-functionally to deliver production-ready AI products.

Lead architecture and maintenance of end-to-end traffic classification ML systems for adversarial domains. Own model lifecycle from offline evaluation to millisecond-edge deployment, build certified offline-to-online data pipelines, establish rigorous evaluation and leakage-prevention, optimize inference under strict latency/cost budgets, integrate scoring with mitigation workflows, and provide cross-functional leadership and mentorship.

The Senior Consultant in AI will design, build, and deliver AI/ML solutions on Azure, engage with clients, and develop project components independently.

As a Machine Learning Engineer, you'll develop and deploy ML models for medical imaging, collaborate with teams, and optimize for production environments.

The Senior Machine Learning Engineer will manage the full ML lifecycle, collaborating with teams to develop scalable systems that improve product decisions and empower monetization strategies.

Lead design, deployment, and monitoring of predictive models for credit risk, collections, and operations. Drive adoption of modern ML and MLOps, mentor data scientists, define strategic roadmap, advise senior leadership, and ensure model governance and regulatory compliance.

Lead the end-to-end machine learning lifecycle for AI products, managing data pipelines and model development to optimize performance and accuracy in production.

As a Lead Machine Learning Engineer at Root, you'll build scalable ML systems, partner with data scientists, and improve operational practices for customer lifetime value modeling.

The Machine Learning Engineer will execute the full ML lifecycle, collaborating on model implementation and improving customer experiences through data-driven insights.

Design and implement scalable ML solutions for customer experience problems. Analyze customer data, research and deploy ML/NLP models, automate model development and validation, and produce performance analyses.

Lead research on humanistic AI for autonomous systems. Collaborate with teams, design experiments, and publish findings in top-tier publications.

Design, build, and ship AI-powered observability features using LLMs and agentic workflows. Rapidly prototype, iterate with real users, integrate AI into alerting, runbooks, and developer workflows, and ensure solutions are scalable, maintainable, and production-ready while collaborating cross-functionally.

Lead and scale Vibrant's data science, ML, and AI function: set strategy and governance, oversee applied research and production ML (especially NLP), ensure HIPAA-compliant deployments, partner with execs and engineering, recruit and mentor teams, and deliver measurable outcomes for crisis services and enterprise initiatives.

Lead a Machine Learning engineering team owning mission, execution, morale, and code quality. Partner with product to deliver roadmap, remove roadblocks, improve processes, coach and grow engineers, and drive development, deployment, and innovation of ML models for recommender, search, and matching systems.

Lead technical vision and roadmap for a company-wide ML platform: define reference architectures, MLOps best practices, security, observability, and integrations; mentor engineers and drive cross-team platform initiatives.

Design, build, and deploy K-12 AI features (lesson planning, grading, tutoring, admin analytics) while ensuring FERPA/COPPA compliance and child-safety guardrails. Measure impact, iterate with curriculum and legal partners, and maintain production-grade documentation, testing, and runbooks for deployed AI systems.

Design, develop, and deploy state-of-the-art deep learning models for multimodal and satellite imagery; own ML architecture, infrastructure, model registries, and inference services; mentor the team, translate product vision into technical roadmaps, and integrate research into production-ready, scalable geospatial AI systems.

Lead end-to-end architecture and delivery of production ML and generative AI platforms on Azure and Microsoft Fabric. Define MLOps, CI/CD, governance, observability, retrieval/LLM patterns, security, cost controls, and operational handoff. Work with data scientists and engineers to translate requirements, defend tradeoffs, and ensure systems run unattended in production.

Work as a hands-on junior ML engineer building generative AI apps, traditional ML models, and MLOps solutions. Implement and maintain model training, inference pipelines, and LLM systems while being mentored by senior engineers and delivering client projects.