Top Machine Learning Jobs in Spokane, WA

Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.

Lead strategy, roadmap, and lifecycle for payer analytics and automation products. Translate market, customer, regulatory, and data insights into product requirements, drive cross-functional delivery with Engineering and Data Science, support go-to-market and adoption, and measure product performance to inform investment and portfolio decisions. Build product practices and partner with stakeholders across sales, customer success, clinical, actuarial, and executive teams.

Lead Growth Engineering architecture and implementation: build Go-based data pipelines, audit/replace martech, create experimentation infrastructure, ship full-stack Go/TypeScript features, productionize ML, review team work, and apply AI-assisted development daily.

Lead and grow a team of 5–6 product developers to deliver high-quality, reliable software. Partner with product and design, champion CI/CD, testing, observability, and provide visibility to leadership while hiring and mentoring engineers.

Lead complex enterprise sales of AI-powered SaaS solutions, build pipeline, manage 6–12 month high-six-figure deals, multi-thread across stakeholder teams, collaborate with marketing/product/customer success, and translate technical AI capabilities into business ROI.

Lead execution of Product Engineering AI and automation initiatives across photonics products. Build and deploy reusable AI/automation tools, transform engineering data into AI-ready systems, and drive cross-functional adoption to improve development, qualification, manufacturing, and reliability processes.

Own the research-to-production pipeline: harden training/evaluation workflows, build packaging/deployment tooling, design release gates, optimize inference and serving for latency/throughput, establish benchmarking and validation, and instrument feedback from production to accelerate research iteration.

Design, train, and productionize underwriting ML models for real-time transaction decisions. Build feature pipelines and training datasets, run experiments, integrate models into batch/real-time systems, and monitor data and model health while collaborating with cross-functional teams.

Lead a consortium advancing AI-driven neurodegeneration research: set strategy, build partnerships, manage multidisciplinary executive team, oversee product development and deployment, secure funding, ensure ethical AI and scientific integrity, and scale the platform to broader biomedical domains.

Lead development of digital twin and simulation models for warehouse operations, deploy AI-driven decision systems, build forecasting and optimization models, validate experiments linking operational metrics to financial impact, and partner with engineers, operations, and external AI/academic partners to scale innovations across fulfillment networks.

The role involves developing and improving TTS/Voice Generation models, training, fine-tuning, evaluating speech models, and collaborating across research, engineering, and product teams in a fast-paced startup environment.

The Business Development Director will lead strategic engagement with Google Cloud, focusing on partnership growth, go-to-market strategies, and revenue generation through cloud solutions.

The Business Development Director will lead strategic engagement with Oracle Cloud Infrastructure, focusing on alliance strategies, go-to-market execution, and revenue growth through innovative cloud solutions.

Lead design and scaling of a unified fraud and risk platform. Own roadmap, fraud metrics, SQL-based analysis, A/B testing, real-time detection models, and cross-functional delivery to balance customer experience and risk mitigation.

Lead fraud detection and mitigation for a conversational BNPL product. Define fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, collaborate on ML models and decisioning workflows, and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational Send Now Pay Later product: define first-party fraud typologies, own repayment and chargeback analytics, manage fraud KPIs, design operational policies, integrate ML and decisioning tools (Taktile), and coordinate SARs and compliance reporting.

Lead fraud detection and mitigation for a conversational BNPL product: define fraud typologies, analyze repayment/chargeback trends, own fraud KPIs, design policies, collaborate on ML models and decisioning workflows, and coordinate SARs with Compliance.

Lead fraud risk for a conversational lending product (SNPL): define first-party fraud typologies, analyze repayment and chargeback trends, own fraud KPIs, design automated/manual blocking policies, coordinate ML/rules with central Fraud and Data teams, and liaise with Compliance on SARs and loan abuse.

The AI Engineer will design and build production systems, focusing on LLM integration, system reliability, evaluation frameworks, and high-quality product features while maintaining strong engineering practices.

As a Principal Product Manager, you'll lead product strategy, collaborate across teams, utilize data for decision-making, and partner with top executives to drive product success.

As a Software Engineering Intern, you will contribute to building features for user-facing products, APIs, and scalable services for machine learning. Responsibilities include crafting code, designing data pipelines, implementing tools, and enhancing technical documentation.

The role involves creating insightful technical content on AI and GPU topics, engaging with researchers, and validating technical claims. Requires independent research and technical writing skills.

The role involves building predictive models for blockchain performance and user behavior, implementing solutions, and owning production prediction pipelines.

Lead end-to-end delivery of LOIS by owning the master program plan, coordinating cross-functional teams (engineering, product, design, ML, data, SRE, operations), resolving blockers and dependencies, establishing lightweight delivery infrastructure and tooling, partnering on release planning, and maintaining transparent status, risks, and accountability to deliver complex features to customers.

Drive new business and expand accounts by selling AI-driven services and software to engineering and manufacturing customers. Manage full sales cycle from prospecting to close, align solutions to measurable ROI, collaborate with technical and marketing teams, and meet revenue targets while attending industry events and maintaining CRM pipeline accuracy.