Top Machine Learning Jobs in Spokane, WA

Leads innovation and enterprise solution strategy for healthcare payment integrity, developing scalable platforms using AI, automation, analytics, and data technologies. Engages senior clients, supports strategic pursuits, shapes transformation roadmaps, and builds partnerships. Oversees solution architecture, enterprise system development, prototyping, governance, performance metrics, and commercialization. The role also mentors cross-functional teams and translates complex healthcare payer challenges into differentiated, market-ready solutions.

Build and operate production machine learning systems for Airbnb Payments, including LLM agents, real-time fraud detection, anomaly detection, personalization, and checkout optimization. Design, train, deploy, monitor, and continuously improve batch and streaming AI applications at global scale. Partner with product, engineering, operations, and data science teams, establish model governance and testing standards, and mentor engineers.

Manage post-sales relationships with strategic customers, drive adoption and business outcomes, coordinate technology delivery projects, oversee customer issues and escalations, facilitate customer meetings and Quarterly Business Reviews, communicate customer needs internally, and track customer status and KPIs. The role supports financial institutions across North America and partners with technical, threat analysis, data science, product, and engineering teams.

Leads the software engineering organization responsible for scalable, reliable applications and systems integrating hardware, embedded software, edge computing, cloud infrastructure, and machine learning. Sets technical strategy, develops engineering leaders, drives product delivery, establishes engineering standards, and ensures operational excellence across national-scale deployments. Partners with Product, Deployment, Data Science, and executive leadership to align roadmaps, resolve risks, and build high-performing distributed teams.

Lead product strategy, roadmap execution, discovery, and delivery for Records Request Management and FOIA solutions. Define AI-enabled capabilities such as document discovery, automated redaction, classification, workflow optimization, and self-service. Partner with engineering, design, security, compliance, legal, sales, and customer teams to deliver secure, compliant products for federal and regulated public-sector customers. Translate FOIA and records-access requirements into product outcomes while supporting adoption, growth, and market expansion.

Develop complex healthcare reports, ETL solutions, databases, and Power BI dashboards for LTSS and Dual Eligible programs. Analyze multidimensional data, ensure governance and accuracy, identify operational improvements, and deliver actionable insights to leadership. The role translates business requirements into scalable reporting solutions, collaborates with clinical, operational, data engineering, and IT teams, and leads testing, process automation, visualization, and large cross-functional data projects.

Drive go-to-market reporting, business intelligence, forecasting, dashboard development, and ad hoc analysis. The role standardizes forecasting processes, maintains analytical documentation, automates reporting and data pipelines using AI-assisted tools, and delivers actionable insights to Sales, Marketing, Engineering, Finance, and executive stakeholders.

Build and lead the organization-wide adoption of generative AI, agentic systems, and AI-assisted development. Design production automation agents, evaluate coding tools, modernize rules-based workflows with ML, and establish responsible AI guardrails. Partner with engineering, product, data, and clinical teams to identify high-impact opportunities, own the enablement roadmap, measure outcomes, and drive adoption through working software, documentation, and hands-on collaboration.

Supports responsible AI governance by developing frameworks, conducting fairness and risk assessments, monitoring regulatory requirements, maintaining compliance documentation, educating stakeholders, and improving AI oversight practices. Works across technical, legal, compliance, risk, and business teams to promote ethical, transparent, accountable, and trustworthy AI systems.

Leads business analysis for AI and automation initiatives, identifying adoption opportunities, developing business cases, gathering requirements, and aligning business needs with technical solutions. The role facilitates stakeholder workshops, supports solution design, testing, deployment, change management, governance, and benefits realization. It also advises executives, monitors outcomes, promotes business analysis standards, and mentors junior analysts.

Monitors and improves AI-powered systems by tracking performance, utilization, KPIs, incidents, and operational trends. Develops reports and dashboards, identifies efficiency opportunities, supports governance and compliance activities, documents procedures, and collaborates with AI engineers, product teams, business stakeholders, and operations leaders to resolve issues and improve adoption.

Annotates, labels, categorizes, validates, and quality-checks structured and unstructured data for AI model training. Maintains dataset accuracy, consistency, documentation, and version control while collaborating with AI engineers, data scientists, and quality teams. The role also supports annotation workflow management, guideline development, calibration exercises, error identification, and continuous process improvement.

Own the strategy, roadmap, development, delivery, and continuous improvement of AI-powered products. Translate business and customer needs into product requirements, coordinate engineering and AI teams, define success metrics, monitor adoption and ROI, and support responsible AI governance. The role also involves stakeholder management, leadership communication, market and technology evaluation, release planning, and ensuring successful product implementation.

Supports AI governance frameworks, policies, risk assessments, compliance reviews, monitoring, reporting, audits, and responsible AI practices. Partners with legal, compliance, security, AI, and business stakeholders to interpret regulations, establish controls, mitigate risks, and improve governance maturity. Tracks regulatory developments, maintains documentation, advises project teams, and promotes responsible AI adoption across the organization.

Assesses and mitigates security risks across AI, machine learning, and Generative AI systems. Responsibilities include threat modeling, vulnerability assessment, security monitoring, incident response, governance, compliance, access controls, policy development, audit support, and advising engineering and business teams on secure AI development and responsible adoption.

Leads operational oversight, performance analysis, governance, and continuous improvement for AI-powered products and services. Develops performance frameworks, executive dashboards, business insights, risk and compliance reporting, and scalable AI operating models. Partners with AI engineers, product teams, and business leaders to optimize workflows, support automation, manage operational risks, and drive cross-functional operational excellence.

Leads AI data annotation operations, managing workflows, resources, production targets, quality standards, audits, and performance metrics. Oversees annotation teams through coaching, recruitment support, onboarding, and performance management. Partners with AI engineers, trainers, product teams, and operational stakeholders to translate requirements into plans. Drives process improvement, automation, tool adoption, data integrity, and operational excellence across AI and machine learning data preparation initiatives.

Leads the strategy, roadmap, development, launch, and optimization of AI-powered products. Defines product vision, requirements, priorities, success metrics, and adoption goals while coordinating engineering, AI, business, executive, and customer stakeholders. Evaluates emerging AI capabilities, supports responsible AI governance, manages product performance, and drives continuous improvement aligned with business objectives.

Identifies business opportunities for AI and automation, gathers and documents requirements, develops business cases, supports solution design and delivery, facilitates stakeholder collaboration, assists with testing and change management, and tracks project outcomes, reporting, and benefits realization.

Owns AI product backlogs, priorities, requirements, user stories, and acceptance criteria. Translates business needs into technical solutions, coordinates stakeholders and delivery teams, supports Agile ceremonies and product releases, monitors adoption and performance, and drives continuous improvement. The role also identifies AI-driven business opportunities, supports responsible AI practices, and contributes to AI product strategy.

Tests and validates AI-powered products, machine learning models, and Generative AI applications. Designs test strategies, executes functional, regression, and user acceptance testing, validates model outputs, identifies defects, analyzes quality metrics, documents evidence, and supports compliance and release readiness. Collaborates with AI engineers, product managers, and business teams to improve testing methodologies, quality frameworks, and AI evaluation practices.

Own the technical systems that convert market signals into revenue pipeline. Build and maintain prediction models, multichannel campaign infrastructure, AI-driven personalization, call intelligence, and signal-to-task workflows. The role requires production coding, applied machine learning, API integrations, SQL, CRM and GTM stack expertise, and practical LLM usage. You will manage live revenue systems, improve attribution and data quality, and balance rapid experimentation with reliability.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

Leads analytical and scientific strategy for Fetch’s Play and Rewarded Apps businesses. Develops predictive and machine learning models, experimentation frameworks, causal analyses, feature pipelines, and measurement systems using Python and SQL. Partners with Product, Marketing, Engineering, and business leaders to improve monetization, engagement, retention, personalization, and marketplace outcomes. Translates complex findings into strategic recommendations, mentors data scientists and analysts, and establishes analytical best practices.

Own the full machine learning lifecycle for search ranking and retrieval, including model training, deployment, serving, feature pipelines, evaluation frameworks, hybrid lexical-vector retrieval, large-scale embedding inference, query understanding, and permissions-aware multi-tenant search. Collaborate with infrastructure, AI, and backend teams to improve search relevance across ClickUp’s platform.