Top AI Research Jobs in Seattle, WA

Execute large-scale human data programs for frontier AI model training and evaluation. Design annotation frameworks, coordinate annotators, maintain playbooks, analyze labeling data, identify policy gaps and systemic errors, and support policy-program delivery with AI labs and internal stakeholders. The role requires strong analytical judgment, communication, ownership, and comfort handling sensitive content.

Design and research agentic AI and reinforcement learning systems: build auto-research and coding agents, develop recursive self-improvement and multi-agent frameworks, run RL experiments (RLHF, DPO, PPO), curate training data pipelines, and publish results at top ML venues.

Develop and apply deep learning models for protein and antibody structure, sequence, and property modeling. Train and evaluate GPU-based models using PyTorch, contribute to computational protein design research, analyze biological and biophysical datasets, document findings, and communicate progress with interdisciplinary scientists and engineers.

Research and develop novel post-training methods such as SFT, RLHF, reward modeling, and preference optimization for text and multimodal LLMs. Analyze model behavior, improve alignment and generalization, mitigate bias, enhance robustness, and publish findings at leading AI conferences. Collaborate with engineers, researchers, and foundation model labs to establish data-driven AI development practices and provide technical and strategic guidance.

Research and explore Vision-Language-Action and Vision-Language Model architectures for autonomous driving. Identify model limitations, redesign architectures, translate academic research into scalable production code, optimize models for supercomputing environments, and mentor engineering teams transitioning into this area.

Develop and deploy deep learning models and petabyte-scale multimodal data curation pipelines for foundation-model training. Responsibilities include document extraction, OCR, layout and table analysis, deduplication, distributed processing, dataset and metric development, experimentation, production scaling, and deployment through NVIDIA Inference Microservices. The role also involves publishing research, creating technical documentation, communicating findings, and mentoring team members.

Own a scoped project in machine learning, AI infrastructure, MLOps, data, robotics, simulation, or world models. Work with production repositories, clusters, and data to deliver a lasting artifact such as a dataset, benchmark, evaluation, or tool, supported by a mentor and technical write-up.

Conduct advanced research and development in LLMs, MLLMs, generative AI, computer vision, and robotics perception. Design scalable multimodal models, optimize them for resource-constrained devices, evaluate performance using real-world datasets, and transition research into products. Collaborate with machine learning, firmware, and hardware engineers, provide technical leadership, mentor junior scientists, and contribute to patents, publications, documentation, and open-source initiatives.

Sell, scope, and package AI consulting engagements across enterprise AI GTM categories. Define assessments, workshops, pilots, and multi-phase programs; develop proposals, justify AI infrastructure, drive adoption of workforce AI and coding assistant services; collaborate with sales, architects, and delivery to ensure clean handoffs and measurable outcomes.

The role involves leading research on multimodal AI systems, improving AI alignment, and collaborating with interdisciplinary teams to develop novel tools and methodologies.