Top Remote Machine Learning Jobs in Salt Lake City, UT

Staff Software Engineer responsible for setting technical direction, architecting scalable distributed systems, shipping high-impact features, and building AI-powered tools for a virtual healthcare platform. The role includes mentoring engineers, partnering with product and clinical leadership, translating user needs into technical solutions, and guiding frontend and backend application development in a fast-paced startup environment.

Lead mainframe modernization and migration architecture for large global clients. Conduct discovery workshops, assess legacy environments, develop transformation and re-platforming solutions, present technical business cases, and advise executives. Partner with sales, consulting, operations, product management, and clients to create solutions, influence technology roadmaps, drive stability and innovation, and support complex opportunities. The role requires deep mainframe expertise across legacy languages, databases, operating systems, infrastructure, and modernization technologies.

Leads enterprise evaluation, governance, and implementation planning for emerging mental health therapeutics, including digital therapeutics, AI-enabled tools, neuromodulation, pharmacotherapies, and technology-enabled care models. Develops clinical protocols, workflows, provider training, safety criteria, outcome measurement, and adoption support. Partners with clinical, technology, research, quality, operations, and growth teams; evaluates vendors and evidence; monitors regulatory developments; and advances responsible innovation across the organization.

Design and deploy production machine learning models and conversational AI agents that transform advertiser briefs into optimized media plans. Build evaluation systems to improve model and agent quality, collaborate across science and engineering teams, investigate ambiguous problems, and iterate using performance data and seller feedback. Communicate effectively with technical and non-technical stakeholders while contributing broadly across machine learning domains.

Leads end-to-end customer onboarding and implementation programs, translating customer goals into project plans, milestones, dependencies, risks, and success metrics. Coordinates Sales, Product, Engineering, Support, Customer Success, trainers, and subject-matter experts without performing hands-on technical implementation or training. Establishes scalable operational frameworks, manages cross-functional accountability, oversees technical readiness, tracks adoption, facilitates stakeholder communications, and ensures customers are prepared for go-live.

Own the full sales cycle across North America, including prospecting, pipeline generation, client meetings, contract negotiation, and closing new business. Build markets across eCommerce, fintech, real-money gaming, and mobile apps while partnering with Customer Success, Marketing, and Product. Represent the company at industry events, monitor mobile advertising trends, and achieve personal and regional revenue targets exceeding $1 million annually.

Lead the roadmap and delivery of an AI-native L1 IT operations agent. Partner with customers, design partners, engineering, and go-to-market teams to ship enterprise-scale capabilities, improve self-service adoption, and build trust through transparent agent experiences and feedback loops. Drive product strategy from discovery through launch in a fast-paced, ambiguous environment.

Build data foundations and end-to-end pipelines, research and implement quantitative pricing, market-making, and risk models for prediction markets, model cross-market dependencies and parlays, develop backtesting/simulation frameworks, monitor model performance and collaborate closely with traders to improve pricing and trading outcomes.

Build and ship production AI systems for enterprise portfolio and planning workflows. Develop anomaly and signal detection, LLM-powered insight synthesis, natural-language planning rule translation, evaluation frameworks, and MCP tools. Partner with external AI providers initially, then establish internal standards for prompt design, model selection, agent architecture, reliability, and evaluation. Collaborate closely with product, design, and domain engineering teams.

Develop client AI/ML solution architectures, assess technical feasibility, define requirements, estimate work, identify risks, and prepare proposal content. Design deployable inference solutions across FPGA, adaptive SoC, GPU, and AI accelerator targets while evaluating latency, throughput, power, memory, data pipelines, and production requirements. Present solutions, support business development, contribute to product and R&D initiatives, and undertake project work. Travel within North America for up to six weeks annually.

Shape company-wide machine learning strategy across credit, fraud, marketing, and growth. Build and improve consumer credit models, establish model governance and responsible modeling controls, and advance experimentation, causal inference, and business measurement. Provide technical leadership, mentor scientists, evaluate AI-assisted workflows, and partner with Legal, Compliance, Risk, Engineering, Product, and Finance to support reliable, compliant, and impactful financial decision systems.

Design, develop, evaluate, and refine machine learning models using Python. Manage training data with MongoDB, build preprocessing and data pipelines, tune hyperparameters, benchmark model performance, document experiments, and deliver data-driven insights. Collaborate remotely with cross-functional contributors and support deployment or operationalization of models in cloud or enterprise environments.

Lead the architecture and delivery of AI/ML initiatives supporting U.S. Air Force and Army predictive logistics and condition-based maintenance programs. Coordinate engineering teams, translate mission needs into technical solutions, guide tradeoff decisions, mentor technical leaders, and ensure AI/ML systems improve fleet maintenance, aircraft availability, and military readiness. The role requires Databricks expertise, technical leadership, stakeholder communication, and an active Secret clearance.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Provides safe, therapeutic perioperative nursing care using the nursing process. Performs assessment, planning, implementation, and evaluation; scrubs and circulates for complex spine and orthopedic surgeries; supports interdisciplinary teams; and assists with managing care delivered by other nursing staff. Requires California RN licensure, CPR certification, and substantial adult operating room experience, including spine procedures. Must work across UCSF Health locations and function effectively under pressure.

Build and deploy advanced machine learning and agentic AI systems that improve Airbnb’s customer service experience. Responsibilities include developing AI assistants, integrating ML products, optimizing model performance, applying LLM techniques such as fine-tuning and RAG, building multi-agent architectures, and shipping scalable production ML systems. The role collaborates closely with Engineering, Product, and Design teams and contributes to emerging AI initiatives from concept through production.

Develop and validate machine learning models, training pipelines, inference systems, and numerical methods using Python. Debug algorithmic and system-level issues, optimize memory usage and performance, design systematic tests, and build reproducible workflows. Review challenging ML engineering tasks, use coding agents in development, document technical findings, and deliver high-quality work independently within deadlines.

Develop and optimize generative AI architectures for material and molecular discovery, including diffusion models, flow matching, and GFlowNets. Integrate reward signals from learned predictors and simulations, manage data ingestion and quality control, develop computational workflows, and deploy production-grade ML capabilities. Collaborate with ML researchers, computational scientists, engineering, product, and R&D teams. The role requires expertise in applied ML, scalable frameworks, cloud platforms, and material discovery, with preferred experience in active learning, Bayesian optimization, guided generation, and agentic or LLM methods.

Develop and deploy machine-learned interatomic potentials for PFAS substitution. Responsibilities include generating and curating first-principles training data, building reproducible cloud and HPC workflows, training and validating ML force fields, running molecular dynamics and quantum chemistry campaigns, predicting physical properties, extending models to condensed-phase and process chemistries, and communicating model uncertainty and domain limitations.

Lead design, development, and production deployment of ML and algorithmic solutions. Own end-to-end ML systems, support distributed production reliability, communicate tradeoffs, and apply generative AI, LLM fine-tuning, RAG, and agentic techniques to product problems.

Develop, implement, and deploy scalable machine learning models for healthcare solutions. Collaborate cross-functionally to integrate models, analyze data to improve performance, ensure data quality, and stay current with ML advancements to support patient care and operational efficiency.

Lead a cross-functional team to develop medical coding software and improve a chart confidence pipeline. Translate stakeholder requirements, guide data scientists and engineers, evaluate recommendations, and report progress to internal stakeholders to increase automation and chart readiness.

Leads the AI product strategy and roadmap, extending governed Alteryx workflows and business logic into enterprise AI agents and LLM ecosystems. Owns the Faster, Better, Cheaper ROI narrative, benchmarks product performance, quantifies customer value, champions trustworthy AI through the VURA framework, and represents the product vision with customers, analysts, and external audiences.

Own the enterprise architecture vision for scalable, high-performance, cost-efficient multi-tenant SaaS systems. Lead AWS cloud optimization, distributed data and AI infrastructure, architectural governance, and Architecture Review Council activities. Guide engineering teams on microservices, event-driven systems, automation, and cloud-native practices while driving GenAI adoption across the software lifecycle. Build prototypes, establish architectural standards, optimize total cost of ownership, and mentor technical leaders.

Leads Dandelion Health’s AI developer business by building pipeline, closing clinical AI data and validation partnerships, recruiting developers into its marketplace, and achieving revenue targets. Develops commercial strategy, manages executive relationships, structures agreements, and collaborates with marketing, product, data, engineering, and clinical informatics teams. Represents the company at industry events and advises on clinical AI validation, regulatory, payer, and competitive dynamics.