Top Research Jobs in Seattle, WA

Lead and scale CrowdStrike's product design organization for the Falcon platform. Own design vision, AI-driven design strategy, design systems, research, and design ops. Partner with product, engineering, and GTM leaders to drive measurable user and business outcomes, hire and develop senior design talent, govern tooling and budgets, and represent design externally.

Lead applied research to advance continuous learning for agents: design and evaluate LLM post‑training and RL methods, implement and deploy experiments at scale, validate research on customer tasks, optimize GPU/distributed training, and mentor engineers while driving cross‑functional technical direction.

Drive applied research to enable continuous learning for agents: design and evaluate LLM post-training methods (fine-tuning, RL, distillation), run large-scale GPU experiments, validate approaches on customer tasks, and help deploy and scale models and infrastructure across the stack.

Lead research in user modeling and personalization, focusing on generative and language models, recommendation and retrieval systems. Build scalable prototypes, partner with engineering to deploy solutions, mentor junior researchers and interns, and publish work at top conferences.

The Senior Product Manager will lead the strategy and roadmap for CrowdStrike's threat detection technologies, collaborating with various teams to enhance cybersecurity products.

This role involves building and optimizing ML systems and frameworks, collaborating with teams to enhance research and development in ML models.

The Director, Research - Power Markets is responsible for leading the QA/QC process for long-term forecasting, advising leadership, and ensuring market insights are effectively integrated into forecasts.

Research and interpret tax code and form changes, translate findings into software requirements, guide programmers, design and execute test cases, validate tax calculations, produce documentation and release notes, serve as subject-matter expert for assigned tax topics/states, support product users, and lead team projects as needed.

Build and productionize machine learning models for fraud and identity risk across the full ML lifecycle: data acquisition, feature engineering, training, deployment, monitoring, and analysis. Research new fraud types, collaborate cross-functionally, write production-ready code, and inform product, sales, and data acquisition decisions.

Lead design, build, and deploy of large-scale data platforms for LLMs, RAG, and agentic AI systems. Hands-on coding, architecting fault-tolerant pipelines, establishing MLOps/DataOps best practices, mentoring engineers, and operationalizing research into production across Exabyte-scale distributed systems.

Lead and scale Headway's clinical research: define research roadmap, design and run studies, perform causal inference and experimentation, analyze large real-world datasets, publish peer-reviewed manuscripts, manage budget and resources, and collaborate cross-functionally to inform strategy and demonstrate impact on patient and provider outcomes.

Lead customer success and research efforts: train and advise customers on Dscout, design and run qualitative research projects, drive adoption and expansion, manage 1–3 Research Advisors, and surface customer feedback to Product.

Lead R&D tax engagements as a senior manager: oversee large projects, design process improvements, maintain operational excellence, advise senior clients, mentor teams, apply specialized R&D tax technical knowledge, and drive business development using firm-wide services.

Provide specialized tax advice on complex areas including R&D tax credits and accounting methods. Analyze tax regulations, optimize client tax positions, document strategies, and uphold PwC technical standards. Collaborate with clients, mentor junior staff, build relationships, and apply analytical and technical writing skills to support tax planning and compliance in a hybrid professional environment.

Lead large-scale collection and analysis of deep and dark web data, build and maintain production-grade automated collection systems (LLM-integrated), operate Docker/cloud-based pipelines and APIs, respond to collection failures and incidents, perform threat actor research, and apply OPSEC tradecraft while collaborating globally.

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

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

The Research Engineer will collaborate with scientists to develop and validate machine learning models for speech technologies, manage scalable training systems, and design tools for accessibility to non-technical users.

The role involves researching and developing large language models (LLMs) with a focus on transformer architecture, data curation, distributed training, and optimization. Responsibilities include conducting experiments, collaborating with teams, and staying updated on deep learning advancements.

Lead applied research and technical direction for agent memory in production AI: define roadmap, design and build memory and retrieval architectures, run large-scale experiments, develop evaluation frameworks, and partner cross-functionally to ship reliable, personalized long-running agent capabilities.

As a Data Scientist at Deepgram, you'll tackle complex audio data challenges, develop scalable data pipelines, and collaborate with teams to advance voice AI technologies.

Lead and conduct high-impact research in software engineering, advancing programming languages, testing, formal methods, and AI integration. Build and maintain research systems, collaborate across multidisciplinary teams, and contribute technical papers and presentations.

Lead research on agentic AI including recursive self-improvement, multi-agent coordination, continual learning, autonomous skill acquisition, and human-AI collaboration. Build evaluation frameworks and synthetic data pipelines, refine scalable architectures, and drive research-to-deployment in collaboration with engineers and product teams.

Lead and conduct research on agentic AI, develop training algorithms and datasets for large language and multimodal models, design evaluation frameworks for reasoning and long-horizon tasks, prototype agentic capabilities (action models, memory, multi-agent orchestration), and drive research to publication and production impact in collaboration with engineers and product teams.

Lead data science and market research for Windows Platform & Developer: define research agenda, build analytical frameworks, track platform health and competitor positioning, run primary/secondary research, instrument telemetry and dashboards, and deliver executive-ready insights to guide investment and roadmap decisions.