Top AI Research Jobs in Seattle, WA

Conduct an AI-focused research project and contribute tools, pipelines, and systems for Atlassian’s Research and Insights organization. The intern will help develop AI-enabled research workflows, collaborate with a research manager, and support faster insight generation for cross-functional teams. The 12-week, full-time internship is based at Atlassian’s Seattle hub and includes mentorship, technical training, and professional development.

Lead research in language models, personalization, recommendation systems, information retrieval, and generative modeling. Build scalable machine learning prototypes, conduct large-scale distributed experiments, publish findings, mentor junior researchers and interns, and partner with engineering teams to deploy technologies serving Snapchat users. The role requires strong Python and PyTorch expertise, research leadership, publications, and experience applying language models to search, ranking, or personalization.

Develop and improve reliable LLM and deep learning capabilities for healthcare applications. Lead research and implementation of scalable real-time machine learning models, Responsible AI metrics and practices, RAG and LLM strategies, and technical innovation agendas. Collaborate with AI scientists and engineers, establish best practices, and drive adoption across service teams.

Leads DigitalOcean’s Applied Research function for agentic AI, setting the research agenda across model routing, memory, observability, and reinforcement learning. Builds and manages a research team, connects research investments to product outcomes, and partners with engineering to ship production capabilities that improve reliability, cost, latency, and task success. Represents the company externally through publications, talks, open-source work, and research collaborations.

The Director of Developer Relations leads the strategy for engaging developers around CoreWeave's AI SaaS offerings, creating content, fostering community, and improving the onboarding experience.

Evaluate AI-generated outputs for clinical accuracy, safety, and appropriateness across patient and provider products. Apply and refine evaluation rubrics, document clinical reasoning, participate in calibration and inter-rater reliability reviews, provide structured feedback to Clinical Product teams, label datasets, and review product use cases. Work within designated systems while maintaining HIPAA compliance and patient confidentiality.

Manage and coach a team of mentors to ensure delivery outcomes for ~200 AI fellows at scale. Build delivery systems, run operational cadence, lead live instruction when needed, translate field signals into curriculum changes, and partner with strategic partners to close the feedback loop between mentors, fellows, and the curriculum team.

Conducts applied AI research for developer tools, building datasets, training large-scale machine learning models, and running experiments involving LLMs, RAG, evaluation, and software engineering. Collaborates with Microsoft Research, GitHub, product teams, engineers, and designers to advance AI capabilities and transition research into products.

Set vision and standards for experimentation, causal inference, and ML; lead high-stakes causal analyses; architect scalable modeling and experimentation frameworks; advise executives; mentor senior data scientists; champion reproducibility and data storytelling; evaluate LLMs and AI agents.

Design and develop large language models and GenAI subsystems for Microsoft 365 Copilot. Lead applied research from product requirements through deployment, ensuring scientific rigor, scalability, security, compliance, and quality. Collaborate with research, engineering, and senior stakeholders; guide architecture, implementation, experimentation, and project planning. Mentor team members, document findings, contribute to ethics and privacy practices, conduct industry outreach, publish research, and support recruiting.

Conduct applied large language model research for Zoommate and meeting intelligence features. Design training, data, evaluation, and benchmarking pipelines; apply post-training methods such as RLHF, RLVR, reward modeling, and preference learning; and develop agentic AI systems with tool usage, function calling, and multimodal capabilities. Collaborate with scientists, engineers, and product managers to translate research into production-ready solutions.

Conduct AI research focused on agentic systems, scientific discovery, literature understanding, human-AI collaboration, continual learning, and model specialization. Interns will collaborate with Ai2 researchers, build systems or models, and author papers for leading AI, NLP, HCI, ML, and information retrieval conferences. The 12-week internship is based in Seattle and requires PhD enrollment, relevant research experience, AI publications, and strong software engineering skills.