Top Machine Learning Engineer Jobs in Seattle, WA

Build and enhance AI/ML platform capabilities spanning model registries, feature registries, governance, data preparation, annotation, lineage, and auditability. Develop cloud-native microservices, APIs, and large language model-powered workflows using retrieval and agent patterns. Improve platform reliability, security, observability, performance, and developer experience while collaborating with engineers, architects, product managers, data scientists, and research partners.

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 technical strategy, architecture, and implementation for ML inference platform services. Design and scale distributed, high-throughput inference systems, collaborate across teams, drive availability, scalability, operational excellence, cost management, and provide company-wide technical direction and mentorship.

The Staff Forward Deployed Engineer drives AI adoption for strategic customers by solving complex cloud infrastructure challenges, developing scalable assets, and influencing product roadmaps through collaboration and technical expertise.

Lead development and production deployment of ML and deep-learning models for sponsored ads selection, relevance, ranking, CTR prediction, auction dynamics, and campaign optimization. Collaborate with product and engineering, run experiments, mentor junior scientists, deploy distributed pipelines at scale, communicate results to senior leadership, and publish research in ML/advertising venues.

Build and operate scalable ML-powered advertising measurement platforms (Brand Lift, Incrementality, Attribution). Develop distributed data processing systems, productionize models, collaborate with data scientists, and implement backend services and data pipelines to deliver privacy-safe, scientifically rigorous ad measurement.

Design, implement, and productionize scalable ML models and microservices; build and maintain data pipelines; prototype solutions, participate in architecture and design reviews, and collaborate with cross-functional teams to deliver ML-powered product features.

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.

Lead design, development, and deployment of scalable generative AI/LLM agent solutions. Collaborate cross-functionally, integrate with AI platform, choose tools, ensure reliability, mentor engineers, and drive roadmap and stakeholder alignment.

Design, build, and ship end-to-end personalization, ranking, and recommendation systems; prototype and scale new ML capabilities (including LLM fine-tuning and agentic workflows); partner with product and engineering to define strategy and deploy models to production at scale; lead architecture and code reviews, mentor engineers, and drive technical direction in a regulated fintech environment.

The Machine Learning Engineer will implement and evaluate ML algorithms, focusing on model development, A/B testing, and cross-functional collaboration to drive innovative solutions for financial challenges.

The Machine Learning Engineer will build and maintain ETL pipelines for image datasets, develop model training infrastructure, and work closely with farmers to implement ML solutions.

The Applied Machine Learning Engineer will develop machine learning models, transform data science prototypes, and build efficient self-learning applications for greenhouse operations.

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.

Lead design, build, and maintain scalable production generative AI/LLM solutions and agentic workflows. Collaborate cross-functionally, define technical roadmaps, integrate with GEICO's AI platform, select tools, ensure production reliability, and mentor engineers to deliver business-facing AI products.

Lead technical strategy and multi-year roadmap for search and vector infrastructure powering Atlassian AI. Drive cross-team initiatives for semantic search, agentic retrieval, and RAG; ensure scalable, cost-effective ML serving with strong reliability and FinOps discipline; mentor senior engineers and raise organizational engineering craft.

Develop scalable machine learning services for data enrichment, managing the ML lifecycle from model training to deployment, and ensuring performance and reliability standards.

Lead end-to-end development and productionization of machine learning systems for causal marketing measurement and optimization, especially cMMM. Build probabilistic and statistical libraries, maintain ML pipelines, implement agentic AI workflows and model validation, review designs and code, collaborate cross-functionally, and mentor ML engineers.

Build and maintain core ML infrastructure and platforms enabling model development, training, deployment, monitoring, and MLOps. Partner with product teams to design scalable, fault-tolerant ML systems, fine-tune LLMs, curate datasets, and drive production launches while mentoring engineers.

Lead design and operation of a large-scale feature store and serving systems for batch and low-latency ML inference. Drive architecture, storage and retrieval patterns, observability, on-call/incident response, and reusable feature registries while mentoring engineers and ensuring performance, correctness, and cost-efficiency.

Lead and grow an ML engineering team responsible for the end-to-end ML lifecycle: data collection, preprocessing, model development, deployment, evaluation and monitoring. Research and implement scalable ML and generative AI techniques (recommendation and agentic systems), mentor and hire engineers, drive pragmatic, business-focused solutions, and communicate technical results to stakeholders.

Build, deploy, monitor, and maintain ML models for production flight systems. Create MLOps pipelines, optimize algorithms for low-latency edge inference, and collaborate with data scientists and flight software engineers to integrate AI into spacecraft and ground operations.

Lead design and implementation of backend systems and statistical models for the Pinterest ads marketplace. Define experiments to measure long-term marketplace effects, design auction and allocation strategies, balance long- and short-term objectives, and drive cross-functional collaboration to improve marketplace operations and insights.

Design, build, and operate GenAI and ML systems for network intelligence. Lead architecture of distributed ML services, implement REST APIs, containerize and deploy with Kubernetes, mentor engineers, and support LLM fine-tuning, retrieval-augmented generation, and big-data workflows.