Top Generative AI Jobs in Seattle, WA

Conduct research in generative modeling with Microsoft Research’s Algorithms group. Interns collaborate with researchers, develop algorithmic innovations, train and improve generative models, present findings, and contribute to research publications and projects during a 12-week internship.

Lead the architecture and technical direction of Generative AI solutions for Life and Annuities insurance operations. Design secure, scalable AI workflows using RAG, agents, vector search, and human oversight. Integrate AI with policy administration, underwriting, TPA, CRM, document, and partner platforms. Establish governance, security, privacy, monitoring, compliance, and nonfunctional standards while guiding cross-functional engineering teams and influencing enterprise technology strategy.

Design and deploy production-ready LLM pipelines and agentic workflows, automate large-scale multimodal dataset pipelines, monitor and improve agent performance, and integrate AI agents with 3D scene composition. The role also involves process documentation, incident response, and continuous improvement, with collaboration across the engine team and company founders.

Lead research and end-to-end development of machine learning solutions involving LLMs, generative models, summarization, LLM-based evaluation, and recommendation systems. Translate advances in generative AI into business applications, optimize generated content for member impact, partner with engineering and ML teams, define technical roadmaps, and communicate complex concepts to stakeholders.

Researches, designs, develops, deploys, integrates, evaluates, and tests AI/ML and Generative AI solutions for mission-focused applications. Builds LLM-based systems using retrieval-augmented generation, agentic workflows, cloud services, and AI/ML frameworks. Participates in the full software engineering lifecycle, collaborates with distributed Agile teams, and communicates technical concepts to technical and non-technical stakeholders.

Build secure, scalable generative AI services, applications, APIs, microservices, and agentic workflows. Own architecture from proposal through production, develop React and TypeScript interfaces, and collaborate with product, data science, and DevOps teams. Implement deployment, monitoring, logging, enterprise security, and reliability capabilities while mentoring engineers and delivering resilient production systems.

In this role, you will design and deploy advanced machine learning and Generative AI solutions, leading projects from conception to deployment while mentoring junior team members.

Design and deliver generative AI and computer vision models and on-device inference for consumer AR experiences. Prototype research advances, optimize models (compression, quantization, distillation), collaborate across teams, and productionize scalable inference pipelines for mobile and wearable platforms.

Design, develop, deploy, and optimize Generative AI applications using LLMs and AI frameworks. Build scalable APIs, automate workflows, integrate foundation models with enterprise systems, and improve model performance through testing and experimentation. Implement monitoring, quality assurance, governance, security, compliance, documentation, and risk mitigation. Evaluate emerging technologies, support proofs of concept, and collaborate with product, engineering, and business teams.

Leads the architecture, development, deployment, governance, and optimization of enterprise Generative AI solutions. Builds applications using LLMs and foundation models, develops prompt engineering frameworks, supports AI strategy, ensures secure and responsible deployment, mentors engineers, evaluates emerging technologies, and translates business needs into scalable technical architectures.

Own and improve the AI Social render pipeline and agent loops that generate on-brand Instagram carousels and reels. Build prompts, structured outputs, tool-calling, and media composition to ship high-quality generative image, video, voice, and music products in collaboration with product, design, and content strategy.

Deploys, integrates, and scales enterprise generative AI solutions in customer cloud environments. Responsibilities include architecting GCP and Vertex AI infrastructure, deploying and optimizing LLMs, building RAG data and vector search pipelines, managing GPU/TPU workloads on GKE, automating infrastructure with Terraform, and advising clients on AI safety, prompt engineering, hallucinations, and inference costs. The role also collaborates with research and platform teams and requires travel to client sites.

Integrate LLMs, RAG, and agentic-AI into secure decision-support software. Design prompts and inference workflows, connect AI to backend services, evaluate outputs, build tests, document designs, and collaborate with engineering, security, and DevSecOps teams to deliver reliable mission-capable AI features.

Senior technical leader responsible for architecting and operationalizing generative AI across the enterprise. Drive end-to-end GenAI architecture, select models and agent frameworks, productionize RAG and agent solutions, ensure observability, guardrails, and cost optimization, integrate with AWS and DevOps toolchains, advise CXO stakeholders, shape AI strategy, and define governance and upskilling programs.

Evaluate, annotate, and quality-check AI-generated text, image, audio, and video content in Spanish. Identify edge cases, ensure guideline compliance, participate in calibration, and provide feedback to improve datasets and annotation workflows for generative AI models.

Own generative AI discovery, scaling, and maintenance for a portfolio of AI characters. Select and standardize models, build pipelines for face consistency and video, train and debug LoRAs, control GPU costs, design prompt architectures, define evaluation metrics and quality gates, and translate creative briefs into technical specs while testing frontier models and tooling to scale the studio.

The Forward Deployment Engineer will deploy and optimize Generative AI solutions, architect scalable infrastructure, and implement data pipelines across multi-cloud environments while collaborating with cross-functional teams to maximize business value.

Architect and optimize large-scale GPU AI infrastructure: profile distributed training/inference, diagnose networking and system bottlenecks, design high-performance clusters, lead POCs, automate benchmarking, and drive technical engagements with customers and internal teams.