Top Machine Learning Jobs in Spokane, WA

As a Machine Learning Engineer, you'll design and implement detection systems, enhance model efficacy, and analyze datasets to improve email detection against security threats.

The Senior ML Engineer will automate component placement on PCBs, working on R&D and production using ML and optimization techniques.

As a Senior/Staff ML Systems Engineer at Quilter, you'll design and maintain ML infrastructure for automated PCB component placement, manage data pipelines, and lead systems design reviews while ensuring project efficiency and reliability within a fully distributed team.

The role involves owning and delivering AI-powered features for Calendly, collaborating with multiple teams, and applying machine learning solutions for product enhancement.

Build and operate high-throughput experimentation and recommendation infrastructure. Design APIs and services, use agentic AI workflows (LLMs) for development, ensure performance and reliability, participate in on-call, mentor teammates, and codify AI-native engineering practices.

The role involves managing the ML lifecycle, developing scalable ML systems, and collaborating on AI-first product solutions focusing on Large Quantitative Models.

As a Senior Machine Learning Engineer, develop LLM evaluation frameworks and agentic AI workflows to extract insights from conversations and optimize customer experiences.

Build and expand in-database ML model catalog: implement new model types, ensure framework parity with scikit-learn/Spark ML, optimize training and inference at petabyte scale, extend SQL-level linear algebra, and collaborate across teams to deliver correct, performant ML features and documentation.

Design, build, and productionize ML systems focused on information extraction, retrieval, ranking, and recommendation. Own full ML lifecycle from feature engineering to deployment, observability, and evaluation. Collaborate with data, infra, and product teams to scale serving for streaming and batch inference and improve models using dataset engineering and graph/embedding techniques.

Design and implement system-level debugging, validation, and observability platforms. Build automated anomaly collection/analysis, visualization and root-cause tools, failure classification and monitoring frameworks. Extend compilers, runtimes and programming interfaces for profiling and instrumentation, improve bring-up and low-level debug workflows, lead cross-functional initiatives, support incident response, and establish debuggability and reliability best practices.

Implement and scale LLM training, fine-tuning, and post-training techniques (RL-based). Build evaluation and data pipelines, debug ML stack issues, optimize training/inference workflows, and ship maintainable ML infrastructure code.

Drive end-to-end ML model inference performance: build kernel- and system-level performance models, optimize kernel microcode and compiler algorithms, debug runtime performance on system and cluster, and develop tooling to visualize and analyze performance data from the Wafer Scale Engine and compute cluster.

Build and maintain data and model layers for an AI decision‑support system: data ingestion, probabilistic record matching, calibrated scoring, provenance‑tracked retrieval and grounded generation, and secure production pipelines. Ensure explainability, auditability, and accredited deployment in a controlled government cloud environment.

Design and build data and model layers for an accredited AI decision-support system: record matching, calibrated scoring, provenance-tracked retrieval and generation, secure ingestion pipelines, model packaging/serving, and instrumentation for audit and accreditation.

Build and operate model validation, quantization, and safety systems for production ML. Develop pipelines for security scanning, optimization (quantization/pruning), routing, prompt management, and evaluation frameworks measuring accuracy, performance, and safety across model lifecycles.

Design, build, and operate core AI capabilities and reusable model integrations for an enterprise platform. Integrate foundation models into production, optimize performance and reliability, prototype new techniques, and support scalable backend services and distributed AI systems in collaboration with cross-functional teams.

Lead design, development, and production deployment of ML models and GenAI/LLM solutions to optimize clinical trial workflows. Build scalable pipelines, monitoring, and CI/CD for robust production use. Collaborate cross-functionally, mentor junior engineers, and communicate technical results to technical and non-technical stakeholders.

Lead and mentor engineering teams while owning platform architecture (React, Node/Express, Postgres, AWS). Drive TypeScript migration, design ETL and AI/LLM integration frameworks, ensure reliability and observability, manage delivery, incidents, and hiring, and define technical strategy for production ML/AI systems.

The role involves product management of AI tools, collaborating with research, engineering, and design teams to develop and enhance products, focusing on user needs and market value.

Build and productionize LLM-powered tools, RAG systems, and agentic workflows. Deploy and operate AI infrastructure on Kubernetes, manage model serving and vector stores, integrate identity and access controls, automate CI/CD and data pipelines, and establish evaluation and observability for deployed services in a defense-oriented environment.

Lead development and refinement of machine learning pricing models (especially reinforcement learning) for Airbnb hosts. Collaborate with data scientists, product, and engineers to design, test, integrate, and monitor models, analyze performance, and prioritize projects to improve host outcomes.

Lead a 4-6 engineer team owning the internal data warehouse and end-to-end ML Ops lifecycle: experimentation, training pipelines, deployment, and monitoring. Provide technical leadership, collaborate with cross-functional partners, define platform standards, and coach engineers to deliver reliable, scalable data and ML infrastructure that powers product analytics and identification features.

Build and prototype supervised ML models to predict ad campaign delivery outcomes (reach, frequency, contention). Own feature engineering, evaluation frameworks, interpretability, and partner with ML engineers to deploy and monitor models at scale. Collaborate across product, engineering, and sales to drive adoption and measure lift versus simulation baselines.

Contractor role to design, harden and productionize ML and LLM systems for enterprise clients: build data/training pipelines, MLOps (deployment, CI/CD, monitoring, versioning), RAG and retrieval systems, benchmarking and cost control, short audits, and client-facing advisory work. Work as part of Janea teams on time-bound engagements.

Lead applied AI research to design and prototype Transformer-based Payment Foundation Models and generative AI solutions for fintech use cases. Drive self-supervised pre-training, fine-tuning, model optimization, benchmarking, and evaluation; collaborate cross-functionally to transition prototypes to production and ensure responsible, secure AI deployment while mentoring engineering teams.