Top Remote Machine Learning Engineer Jobs in Seattle, WA

The Machine Learning Engineer will work on unstructured data classification and clustering, utilizing their expertise in AI systems and various programming languages.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

Develop, fine-tune, and deploy LLMs for NLP tasks; design and maintain large-scale data pipelines; analyze time-series data; implement data-driven models and OCR systems; ensure data quality; collaborate cross-functionally; and research and apply best practices to improve internal data processing tools and infrastructure.

Lead architecture and technical strategy for Lyra's ML and generative AI platform. Design training, fine-tuning, and low-latency inference infrastructure (RAG, vector DBs), set ML/AI SDLC standards (dataset lineage, CI/CD, evaluation, monitoring), partner with cross-functional clinical and product teams, mentor senior engineers, deliver prototypes and critical code, and ensure secure, reliable production ML systems.

The Staff ML Engineer will design and operate Samsara's ML platform, collaborating with teams to enhance ML features and improve safety outcomes. Responsibilities include overseeing system reliability, leading technical direction, and mentoring engineers.

Design and build AI assistants for interactive 3D workflows: build NLP pipelines, integrate cloud and client LLMs, design knowledge graphs, fine-tune foundation models, prototype AI features, and improve personalization and system performance.

The Lead AI/ML Engineer evaluates emerging technologies, leads teams in adopting solutions, and provides technical leadership while mentoring junior specialists.

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.

As a Senior Staff Machine Learning Engineer, you will drive AI product development, collaborate with cross-functional teams, and enhance ML models at scale.

Design and productionize ML models and agentic systems for intraday financial alpha generation. Build data pipelines, experiment frameworks, and portfolio optimization methods. Collaborate with quants and engineers to move models from research to live trading environments.

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.

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.

Lead design and delivery of large-scale, production ML systems and generative-AI solutions. Drive end-to-end development, build high-performance ML code, extend ML frameworks, integrate models at scale, mentor engineers, and champion operational excellence for real-time distributed platforms.

The role involves building ML systems, improving models across product surfaces, and leveraging data for personalization and user engagement.

The Senior Databricks AI/ML Engineer will design and deploy AI/ML solutions in the insurance sector, focusing on Databricks architecture, data pipelines, and MLOps workflows, while collaborating with various teams to operationalize models and optimize processes.

As an ML Ops Infrastructure Engineer, you will design CI/CD pipelines for ML, maintain deployment systems, and implement monitoring while collaborating with research teams to ensure model quality and performance.

The engineer will build and operate AI/ML infrastructure, managing services on AWS and bare metal, using tools like Kubernetes and Terraform.

Build, train, fine-tune, evaluate, and deploy machine learning and LLM models for production. Create monitoring, evaluation, and feedback tooling, evaluate new techniques, and collaborate cross-functionally to ship ML-powered features.

The Staff Machine Learning Engineer will lead AI initiatives, architect LLM systems, design evaluation frameworks, and mentor engineers.

Design, build, and deploy machine learning systems for controlling quantum processors. Develop reinforcement learning and Bayesian methods for QEC calibration, and work cross-functionally with product and R&D teams.

Lead design, build, and operation of end-to-end production ML systems (feature pipelines, training, inference, experimentation, monitoring) for ranking, recommendation, rewards, ROAS/LTV prediction, personalization, and offer optimization in a high-scale AdTech environment. Provide hands-on technical leadership, mentor engineers, drive MLOps and observability standards, and partner with Data Engineering, BI, Product, and business teams to connect models to outcomes.

As a Staff Machine Learning Engineer, you will develop, productionize, and deploy ML models for clinical use, ensuring real-world effectiveness through hands-on coding and infrastructure building.

Develop and deploy end-to-end machine learning solutions, collaborate with cross-functional teams, and implement MLOps frameworks while monitoring model performance in production.

As a Machine Learning Engineer in Computer Vision, you'll develop AI solutions using advanced algorithms, focusing on image classification, segmentation, and video processing tools.