Applied AI Scientist in Seattle

• Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, or a closely related discipline. Equivalent professional experience with demonstrated technical expertise may also be considered. \n

• 3-6+ years of industry experience developing applied AI or machine learning solutions. \n

• Demonstrated success delivering AI-powered applications from concept through production deployment. \n

• Strong programming experience with Python and/or C++. \n

• Hands-on expertise with modern machine learning frameworks such as PyTorch, TensorFlow, or JAX. \n

• Deep understanding of transformer architectures, neural networks, generative IA, and natural processing. \n

• Experience developing AI applications capable of operating reliability at enterprise scale.

nWe are seeking a highly skilled Senior Applied AI Scientist to help drive the next of intelligent machine learning solutions. This individual will play a key role in researching, designing, and deploying advanced AI capabilities with an emphasis on generative AI, autonomous agent architecture, large models (LLMs), and deep learning.\n \n Most Important Responsibilities

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• Research, design, and implement advanced machine learning models focused on generative AI, autonomous AI agents, and deep learning technologies. \n

• Develop production-ready AI systems utilizing LLNs, tool integrations, retrieval-augmented (RAG), planning workflows, and multi-step reasoning capabilities. \n

• Build scalable AI solutions that support intelligence, fraud detection, risk analytics, search optimization, and related predictive applications. \n

• Partner with software engineers, machine learning engineers, product managers, and technical stakeholders to define requirements and deliver high-impact AI capabilities. \n

• Design and optimize transformer-based architectures, graph-based learning models, and modern information retrieval techniques. \n

• Train, fine-tune, and customize foundation models for specialized business use cases when commercially available models do not meet performance expectations. \n

• Analyze large, complex datasets to uncover insights, improve prediction accuracy, and enhance machine learning performance. \n

• Evaluate emerging AI research and rapidly prototype new techniques for practical production use. \n

• Contribute to the architecture, scalability, monitoring, and continuous improvement of enterprise AI platforms. \n

• Present technical findings, research outcomes, and implementation strategies to engineering leadership and cross-functional teams. \n

• Mentor junior scientists and contribute to technical best practices across the AI organization. \n