Top AI Research Jobs in Seattle, WA

Develop and deploy scalable AI/ML models for multimodal neuropathology data, including digital histology, spatial transcriptomics, MRI, and clinical metadata. Build data pipelines and distributed training infrastructure across cloud and GPU environments. Apply computer vision, foundation models, Bayesian methods, and causal modeling to understand neurodegenerative disease progression. Collaborate with scientists, neuropathologists, computational biologists, and engineers, while publishing research and releasing open-source tools, models, and code.

Lead the research roadmap and grow an applied AI research team while remaining hands-on with reinforcement learning and post-training. Collaborate with customer research labs, improve training environments, datasets, and trajectories, and translate research into production-ready deliverables. Recruit at least four researchers and partner with engineering, delivery, and go-to-market teams.

Develops original concepts and working prototypes that demonstrate emerging AI video, world model, and agentic system capabilities. The role combines creative direction, coding, interaction and visual design, storytelling, and research exploration. Responsibilities include collaborating with researchers and engineers, rapidly testing ideas, and packaging explorations into polished demos, films, and interactive experiences that communicate the potential of new AI systems.

Conduct applied research on AI models, inference systems, accelerator ecosystems, and data center infrastructure. Evaluate emerging technologies and translate findings into engineering, product, infrastructure, financial, and commercial strategies. Own technical views on GPU hall capacity, power, cooling, density, cost, and performance. Support customer and partner discussions, infrastructure diligence, benchmarking, and technology investment decisions. The role requires hands-on inference, compiler, kernel, runtime, and multi-accelerator experience, with up to 25% international travel.

Define Cisco’s multi-year AI research strategy and architecture for purpose-built large language models in networking, security, data center, and observability. Lead massive-scale pre-training, continual pre-training, reinforcement learning, alignment, agentic framework, and autonomous operations initiatives. Advise executive leadership on production-scale AI ecosystems, publish influential research, lead academic initiatives, and mentor senior research talent.

Conduct cutting-edge machine learning and AI research applied to Pinterest products. Interns lead projects, develop and evaluate production ML systems, work across algorithms, data engineering, infrastructure, training, inference, and product integration, and write sustainable code. They will use AI-assisted development tools, validate generated outputs for correctness and security, investigate bugs, collaborate with researchers and engineers, and communicate solutions to open-ended technical problems.

Join Welo Data’s Swahili contributor network for potential remote AI projects involving text, image, or audio annotation; search, advertising, or chatbot evaluation; prompt creation; and cultural and linguistic accuracy review. Applicants must be native or near-native Swahili speakers based in Kenya, Tanzania, or the Democratic Republic of Congo, with English proficiency, digital-tool comfort, and strong attention to detail. Projects vary in duration and compensation, with details provided before participation.

Develops reinforcement learning algorithms, model architectures, and experimental infrastructure for frontier-scale training. Responsibilities include scaling RL across model size, context length, and compute; diagnosing numerical, algorithmic, and systemic failures; optimizing performance and cost; and managing end-to-end large-scale runs. The role requires rigorous experimentation, distributed training expertise, quantitative modeling, and strong programming across the ML systems stack.

Lead independent AI research on large language models and agentic systems, from hypothesis through training, evaluation, and production deployment. Develop post-training methods, reasoning and planning capabilities, evaluation benchmarks, synthetic data pipelines, and enterprise AI improvements. Publish at leading conferences, shape model strategy, and mentor researchers and engineers.

Conduct independent biomedical AI research to personalize cancer treatment using clinical, genomic, and molecular data. Develop statistical models, machine learning and deep learning pipelines, predictive methods, R packages, and Shiny applications. Validate computational approaches and collaborate with clinicians, researchers, and industry partners on translational studies involving treatment outcomes, synthetic control cohorts, patient similars, and digital twins.