Top Machine Learning Jobs in Seattle, WA

Lead teams building computer vision and machine learning for AI dash cameras, managing model lifecycle, coordinating with cross-functional teams, and driving AI product development.

Lead AI strategy, partner engagements, and operational execution across CrowdStrike products. Align R&D and product delivery to company AI strategy, prioritize initiatives, set organizational goals, drive thought leadership, and create order in complex cybersecurity AI environments while delivering customer value.

Intern will own and deliver a scoped engineering project on Deepgram's voice platform (ASR/TTS/LLM/API/SDK), ship reviewed code to staging/production, use AI in development workflows, improve a component measurably, and present results with documentation.

Lead and manage the underwriting and credit risk model validation team, perform hands-on independent validations of complex ML models, assess data integrity and performance, identify and remediate model weaknesses, partner on MRM framework improvements, and coordinate with audit, compliance, and controls to meet regulatory standards.

Lead the development of machine learning systems for Cedar Pay's Personalization Engine, manage ML engineers, and ensure ML model effectiveness.

The Staff Machine Learning Engineer will lead ML initiatives, develop ML systems, collaborate with cross-functional teams, and mentor junior engineers to optimize healthcare intake workflows using advanced machine learning techniques.

Lead the design and implementation of critical Safety AI initiatives, focusing on real-time perception systems for edge devices and optimizing models for production deployment in safety-critical environments.

The role involves deploying and supporting CrowdStrike's AI capabilities in cybersecurity, optimizing threat detection, and mentoring teams on AI solutions.

Drive end-to-end ML solutions for search, ranking, recommendations, and personalization. Build production model pipelines, scalable data pipelines, monitoring, A/B testing, and collaborate with cross-functional teams to translate business needs into measurable, reliable AI systems.

This role involves developing AI applications, focusing on LLMs and agent frameworks, and requires research skills in ML with experience in data strategy.

Lead design and development of scalable AI architectures and frameworks, collaborate with research and product teams, deploy and optimize ML/LLM systems in production, guide ML platform strategy, perform code reviews, mentor engineers, and evaluate emerging AI technologies for business impact.

Lead design, implementation, and scaling of end-to-end ML systems including feature pipelines, model training, evaluation, deployment, and monitoring. Partner with cross-functional teams to productionize models, apply MLOps best practices (automated retraining, CI/CD, drift detection), and mentor engineers to maintain high code and system quality.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Partner with healthcare clients to discover, design, and deliver production-grade AI/LLM solutions. Architect cloud-native systems, build and deploy ML/LLM applications, prototype rapidly, troubleshoot distributed systems, and lead client engagements to drive measurable business outcomes while collaborating across product, platform, and engineering teams.

Build and operate scalable machine learning training platforms and pipelines across AWS and other cloud environments. Optimize GPU-based single-node and distributed training for performance, cost, stability, and reproducibility. Develop Kubernetes infrastructure, GenAI and LLM fine-tuning workflows, observability, CI/CD automation, governance controls, and developer self-service tools. Partner with data engineering and platform teams while applying secure, responsible AI-assisted development practices.

Design, build, and productionize real-time fraud detection models. Advance adversarial-domain techniques (graph, sequence, anomaly detection), design robust evaluation for biased/delayed labels, ensure low-latency reliable deployment, monitor models in production, and collaborate with data scientists and stakeholders to translate fraud signals into modeling solutions.

The Staff Machine Learning Engineer will lead the development of search ranking systems, focusing on machine learning models for relevance, quality, and personalization through various advanced techniques and collaboration with cross-functional teams.

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.

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.

Lead the delivery of ML initiatives, define roadmaps with cross-functional teams, and develop ML models for conversational agents and smart tools, enhancing customer support.

Design, train, and deploy ML and deep-learning models for sponsored ads: relevance, ranking, click-through prediction, auction and bidding optimization. Lead models from ideation through production, collaborate with product and engineering, set modeling standards, mentor junior scientists, and communicate results to leadership.

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Manage and mentor a global team of data engineers and data scientists, align technical delivery with business goals, allocate resources, improve delivery processes, drive automation and technical evangelism, and collaborate with sales and account teams to support growth.

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.