Top Remote Machine Learning Jobs in Spokane, WA

Design, build, and deploy conversational AI solutions on the Kore.AI platform. Create and configure chatbots (intents, entities, dialog flows), integrate with back-end systems and REST APIs, and develop responsive user interfaces using ReactJS alongside UI/UX designers.

Lead digital transformation engagements by developing strategy, advising executives, and delivering end-to-end projects. Drive adoption of cloud, AI, data analytics, and emerging technologies while managing multidisciplinary teams, ensuring compliance, and measuring outcomes through KPIs.

Lead enterprise design and deployment of a global operating model for bovine IVF production. Drive standardization, SPC, automation, AI/analytics, Lean/Six Sigma, quality systems, and continuous improvement to scale consistent, high-throughput production across labs and field operations.

Senior presales role driving planning solution strategy for large enterprises. Lead executive discovery, design tailored demos, articulate value of Workday Adaptive Planning and AI-enabled forecasting, support sales motions (RFPs, workshops, POCs), mentor peers, and travel up to 50% to engage customers and represent Workday at events.

Lead a team of analysts to advance fraud and safety operations through analytics, ensuring robust data products and compliance while empowering stakeholders with actionable insights.

Lead go-to-market strategy for Magnet Forensics' security, incident response, and AI product portfolio. Influence executive, product, and sales stakeholders; translate technical AI/ML and forensic capabilities into messaging; develop buyer personas; create sales enablement and demand-generation content; evangelize products at events; partner with competitive intelligence and customer advocacy; mentor product marketing team members.

The Software Engineering Manager will lead a team in creating tools for Cengage Learning Platforms, focusing on AI innovation, team growth, agile delivery, and collaboration.

Design reusable software tools and subsystems, lead implementation and estimation, ensure systems integration and multi-platform compatibility, drive projects to completion, and perform feasibility analyses for future projects.

Lead architecture and engineering for AI-powered applications across backend, mobile, and desktop. Own reliability, observability, APIs, and model integrations. Grow and mentor a small senior applications team, drive production readiness, and make high-impact tradeoffs across latency, cost, security, and user experience to deliver reliable, low-latency AI-enabled workflows.

Transfyr seeks adaptable builders for various roles across CS, biology, and business. Responsibilities include developing sensor stacks, data harmonization, computer vision, machine learning systems, lab operations, project management, and business development.

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.

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.

The AI Engineer will build data pipelines and improve ML systems for reliability in production. Responsibilities include schema design, data infrastructure, and model evaluation, ensuring high-quality outputs and integration with product workflows.

Lead the development of production-grade machine learning systems, build ML pipelines, and optimize models for deployment and performance.

Independently validate and monitor machine learning models for credit underwriting, credit risk, and fraud detection. Identify model weaknesses, recommend improvements, and collaborate with model owners to remediate findings. Support and maintain the Model Risk Management framework and coordinate with Internal Audit, Internal Controls, and Compliance on audit and regulatory requests.

As a Machine Learning Engineer II, you will enhance AI systems for automating customer operations, develop evidence extraction pipelines, and collaborate with teams on model development and deployment.

Lead end-to-end data science and AI projects: translate business problems, prepare large datasets, build and deploy predictive/prescriptive and generative AI models (including LLM fine-tuning and prompt engineering), communicate insights to stakeholders, and promote responsible, explainable AI practices across consulting engagements.

Lead AI risk assessments, reviews, and monitoring to support AI governance at a financial institution. Evaluate internal and third-party AI solutions, maintain an enterprise AI inventory, analyze controls and risk metrics, build dashboards and reports, and provide quantitative and advisory support across stakeholders to enable responsible AI adoption and regulatory readiness.

Lead complex transaction monitoring investigations to detect money laundering, terrorist financing, and fraud. Analyze large datasets and payment activity, coach analysts, review investigation quality, improve monitoring rules and processes, collaborate cross-functionally, and support regulatory reporting and audits.

Lead product vision and roadmap for data and analytics products (ML, generative AI, data pipelines, BI). Translate business needs into developer-ready features, maintain backlog, collaborate with engineering and stakeholders, track KPIs, and drive adoption and continuous improvement.

Build and maintain full-stack features, data pipelines, and cloud infrastructure for large-scale ad management and analytics. Integrate with advertising platform APIs, apply ML/NLP for forecasting and recommendations, collaborate with product and marketing teams, and use AI coding tools to accelerate development.

Lead architecture and development of high-scale systems for ingesting, processing, storing, and serving user profiles. Drive cross-team initiatives across APIs, streaming infrastructure, and data processing. Improve scalability, reliability, data quality, observability, and performance while mentoring engineers and partnering with product, data, and ML teams to enable segmentation, personalization, and ML use cases.

Lead causal inference and measurement efforts for payments, build and evaluate ML/AI and LLM-based models, optimize fraud mitigation and loss strategies, simulate interventions, and communicate findings to stakeholders to drive product and risk decisions.

Lead Lean transformation initiatives and AI coordination to enhance process excellence across multiple business functions, coaching teams and ensuring integration with operational standards.

Own end-to-end analysis of payments, chargebacks, and user lifecycle to identify drivers of net loss. Deep-dive into fraud cases, design and evaluate A/B experiments and risk-rule changes, build monitoring dashboards, partner with Risk, Payments, and Product to convert insights into mitigations, and run ad-hoc analyses of emerging loss trends.