Top Senior Machine Learning Jobs in Spokane, WA

Design, build, and operate production AI platform capabilities and agentic workflows. Collaborate on architecture, write production-quality backend code, mentor developers, troubleshoot production issues, and drive adoption of reusable AI frameworks, MLOps practices, and cloud-native intelligent services across a multi-tenant SaaS dental platform.

The Principal Architect will lead AI/ML solutions, mentor teams, and collaborate with clients to design and deliver scalable systems using Google Cloud technologies, ensuring compliance with ethical standards.

The Data Science Manager will lead and mentor a team of data scientists, drive data-driven decision-making across Stripe, and collaborate with various stakeholders. Responsibilities include managing projects, recruiting, and contributing to broader data science initiatives.

Senior product engineer owning end-to-end product surfaces for a computer vision platform. Ship features from idea to production, prioritize roadmap, design UX details, integrate APIs, improve annotation and model-assisted labeling, and collaborate with engineering and users to drive product impact.

The role focuses on leading editorial efforts to define industry narratives around energy infrastructure, translating regulatory changes into strategic content for executives, and influencing sales and positioning through high-impact communications.

As VP of Engineering, you'll lead the application engineering strategy and team, integrating AI capabilities into products while ensuring quality and execution across backend, mobile, and desktop platforms.

Pre-sales technical lead for federal accounts who builds and delivers AI/ML and GenAI demos, designs compliant solutions (FedRAMP/DoD IL/NIST/FISMA), supports pilots/proofs, advises on data readiness and security, and partners with sales to drive federal adoption.

The Senior Director leads research in Application Platforms & AI, guiding clients in AI strategy and market trends, while producing critical research content and engaging with clients directly.

The Regional VP of Sales drives sales for Lirio, focusing on healthcare systems. Responsibilities include pipeline generation, relationship building, and strategic sales management.

Lead vision, strategy, and roadmap for Fetch's referral growth engine. Own end-to-end referral lifecycle, build incentives, experimentation, AI-driven personalization, and cross-functional initiatives to drive MAU growth and durable network effects.

The role involves researching, developing, and implementing AI solutions, optimizing models, integrating AI into workflows, and collaborating with teams to enhance business efficiency.

Lead the design, development, and deployment of scalable machine learning and AI systems for enterprise clients, guiding architecture and mentoring engineers.

The Senior Display Media Account Manager develops and executes digital media strategies, focusing primarily on Amazon DSP, while fostering client relationships, providing analytics, and driving business growth for clients.

Contract senior recruiter responsible for sourcing and hiring ML/AI talent, managing full-cycle recruitment, partnering with hiring managers, and supporting diversity and employer-branding efforts.

The job involves building responsive web dashboards, advanced data visualizations, and analytics tools for unmanned aircraft systems, requiring a deep understanding of UI/UX design and data presentation.

Lead a team of ML engineers to design, build, and productionize LLM/GenAI and agentic systems (retrieval, tool use, orchestration). Collaborate cross-functionally with MLOps, product, and data science, review code, mentor engineers, run technical evaluations and proofs-of-concept, and ensure reliable, safe ML deployments for a large SaaS product.

Responsible for fine-tuning state-of-the-art LLMs, optimizing models for deployment, and driving AI architectural decisions. Collaborate with cross-functional teams to develop impactful AI products.

Embed with strategic higher-education partners to design, build, and ship production AI and platform integrations (LLM/RAG, LTI) across Cengage products. Own end-to-end deployments, write production code, ensure compliance and accessibility, evaluate AI safety and outcomes, mentor FDEs, and translate field learnings into product roadmap contributions.

The Machine Learning Engineer will develop and maintain scalable ML systems, collaborate across teams, and innovate solutions for real-time applications to enhance customer experiences.

Drive delivery excellence and customer success by leading the architecture and implementation of AI/ML solutions while ensuring account growth and client engagement.

Design, build, deploy, and operate production-grade AI/ML and LLM systems (including RAG and agentic pipelines). Integrate AI capabilities with product workflows, run evaluation and monitoring, optimize model and system performance, and apply strong engineering practices (testing, CI/CD, versioning).

Build and scale production-ready generative AI applications: design multi-agent workflows, implement advanced RAG with vector DBs, integrate/swap LLMs, fine-tune models, and deploy scalable FastAPI/Docker services with monitoring and optimization for latency, cost, and reliability.

Design, develop, and deploy machine learning and deep learning models; build end-to-end ML pipelines including preprocessing, training, evaluation, and deployment; perform feature engineering on large datasets; deploy and monitor models on cloud platforms; collaborate with cross-functional teams to integrate AI solutions and retrain models as needed.

Design, develop, fine-tune, and deploy generative AI models (GANs, VAEs, transformers). Collaborate with cross-functional teams, troubleshoot model issues, document work, and communicate technical concepts to non-technical stakeholders while staying current on research.

Operate and monitor ML/AI models and agentic systems in production. Build AI observability, logging, tracing, and evaluation pipelines. Monitor LLM outputs, detect drift and model degradation, and maintain data/feature pipelines. Develop CI/CD, model versioning, experiment tracking, and automate alerts and incident remediation while collaborating with data scientists and platform teams.