Top Senior Machine Learning Jobs in Seattle, WA

Design, productionize, and deploy machine learning forecasting models for ad inventory and campaign performance in low-latency, real-time advertising environments. Build scalable simulation, budgeting, and pacing systems; collaborate with cross-functional teams to deploy models at scale and optimize ad monetization.

Lead integration of state-of-the-art AI/ML into Netflixs content platform and publishing tools. Build roadmaps, incubate ML systems from idea to production, partner with researchers and engineers, automate promotional asset creation, optimize distribution and localization, and measure impact with data-driven evaluation.

Build, ship, and own end-to-end ML models for vendor matching, order routing, and supply-chain optimization. Partner with data engineering to productionize models, quantify impact, document work, and implement decision logic components toward agentic AI in a fast-moving healthcare startup environment.

Lead causal inference and measurement efforts for payments, build and evaluate ML/AI and LLM-based models, optimize fraud mitigation and loss strategies, simulate interventions, and communicate findings to stakeholders to drive product and risk decisions.

Lead the development and implementation of machine learning solutions, managing a team to drive strategic decisions and innovative business outcomes.

Lead a multidisciplinary team to design, deploy, and optimize ML-driven bidding systems and measurement for a nine-figure marketing budget. Drive experimentation, partner strategy, media-mix optimization, and end-to-end measurement while aligning Marketing, Analytics, Engineering, and Product to improve marketing ROI and scale performance.

Lead end-to-end deployments of AI for hotel revenue teams: integrate PMS/CRS/RMS, ship ML/optimization models and agentic workflows, validate recommendations with revenue managers, define metrics, drive adoption, and hire/mentor the forward deployed engineering team.

Conduct research and develop identity and probabilistic-matching models for Netflix's ad-supported tier. Apply ML and statistical methods, ensure privacy-preserving solutions (differential privacy, anonymization), prototype and deploy production models, deliver datasets and tools, and partner with product, engineering, and business stakeholders to measure and drive impact.

Lead end-to-end ML projects for fraud detection: develop, deploy, monitor, and iterate production models and experimentation frameworks. Improve model performance, build scalable ML pipelines, collaborate with Product/Engineering/Risk, and champion testing, documentation, and model-monitoring best practices to ensure robust, high-impact solutions.

Build and deploy statistical and machine learning models, design experiments, analyze product and operational metrics, drive data collection, forecast performance, and communicate insights to product, engineering, and business teams.

The Staff Machine Learning Engineer will lead ML systems development for personalization and ranking, design architectures, and mentor team members, ensuring reliable operations at scale.

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

The Senior Machine Learning Engineer will utilize deep experience and insights to enhance communication and digital interactions through advanced machine learning tools and algorithms, focusing on unstructured data.

Design, implement, and test scalable, low-latency ad selection systems using rule-based and ML solutions. Build and maintain microservices, observability, and metrics; translate product stories into tasks; mentor teammates; participate in on-call rotations and own domain areas.

Build and own production applied-AI systems focused on information retrieval and LLM pipelines. Design experiment frameworks and evaluation pipelines, optimize for metrics (precision, recall, F1), run benchmarks, perform failure analysis, and iterate to improve real-world system performance at scale.

The Manager of Machine Learning Engineering will lead a team to develop and deploy scalable ML solutions, oversee project direction, and ensure responsible AI practices while fostering cross-functional collaboration at Disney.

Lead the machine learning team to develop scalable AI solutions, mentor engineers, and oversee complex ML projects within the Ad Platforms organization.

Design, build, and deploy large-scale distributed AI/ML applications (IVR, voice, chat) including multi-agent systems and LLMs. Collaborate with product and data science, ensure reliability, scalability, monitoring, and bring research-driven innovations into production using cloud, containers, and IaC practices.

Lead machine learning initiatives, oversee product development, improve execution pace, enhance team reliability, and foster team growth at Hightouch.

Act as the technical lead in enterprise sales: run technical discovery, design and deliver customized demos and POCs, assess API/cloud/model fit, address technical objections, support contract negotiations, and relay product feedback to engineering while staying current on AI/ML trends.

Lead ML-focused silicon architecture: explore algorithms/architectures for AI accelerators, build bit-accurate C-models and test platforms, drive architecture exploration, and collaborate with hardware/software teams to support design, DV, emulation, and kernel development through the silicon cycle.

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

Designs and operates large-scale AI infrastructure for GPU-based training and inference. Responsibilities include distributed training platforms, scheduling, storage and networking performance, observability, fault tolerance, security, automation, cost optimization, and developer tooling. Partners with ML research teams on capacity planning and maintains operational documentation. Requires expertise in GPU clusters, distributed systems, Linux, networking, cloud ML services, and software engineering, with proficiency in Python and Go or C++.

Optimize large-scale AI training and inference systems for throughput, latency, and cost. Responsibilities include GPU and kernel optimization, distributed training, model compression, LLM serving, compiler optimization, data pipeline tuning, benchmarking, hardware evaluation, and performance regression analysis. The engineer will collaborate across ML and platform teams, document optimization practices, evaluate emerging technologies, and mentor junior engineers.

Designs, evaluates, and deploys applied machine learning systems across language, vision, recommendation, and structured-data domains. Responsibilities include developing scalable training and inference pipelines, conducting rigorous experiments, optimizing models, managing data quality, implementing safety and fairness evaluations, and translating research into production solutions. The role also involves collaborating with product and platform teams, documenting findings, mentoring engineers, and influencing AI strategy.