Top Generative AI Jobs in Seattle, WA

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.

Defines organization-wide standards and architectures for LLM applications, including prompt engineering, agentic systems, retrieval-augmented generation, evaluation frameworks, safety guardrails, and developer tooling. Ships and reviews production LLM solutions, optimizes latency, cost, and quality, leads red-teaming, mentors engineers, and guides responsible AI adoption across product teams.

Lead research and development of generative AI and recommendation solutions for Netflix promotional assets and content discovery. Responsibilities include tracking LLM research, developing summarization and LLM-as-judge systems, training and evaluating models, optimizing generated content for audience impact, partnering with engineering teams, defining ML roadmaps, and communicating technical strategies to stakeholders.

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.

Design, develop, and deploy production-grade generative AI and agentic systems (LLMs, RAG, AI agents). Build document intelligence, retrieval pipelines, vector search, and monitoring frameworks. Collaborate with risk, compliance, product, and engineering to deliver scalable, governed GenAI solutions in regulated environments.

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.

As a Software Engineer focused on generative AI, you will design scalable AI services, optimize APIs, and collaborate with cross-functional teams to enhance enterprise-wide transformation.

Reviews and evaluates AI-generated text, translations, audio, images, video, and other data; identifies errors and quality issues; annotates and labels content; provides written feedback; follows detailed project guidelines; and completes independent assignments within deadlines.

Design, build, and deploy production-ready intelligent solutions using machine learning, deep learning, generative AI, and large language models. Develop RAG pipelines, integrate AI services, manage model deployment and MLOps workflows, and apply strong software engineering practices. Collaborate with engineering, product, and data teams in a distributed environment to create scalable, reliable, responsible AI-powered products.

Build and deploy AI-powered applications for customers, combining Python and TypeScript application engineering with LLMs, generative AI, agent orchestration, APIs, distributed systems, and cloud platforms. Partner with stakeholders to understand requirements, design scalable architectures, rapidly prototype solutions, integrate AI capabilities, and evolve proofs of concept into production systems. The role also involves customer-facing consulting, technical decision-making, and collaboration throughout the solution lifecycle.

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.

Lead design, prototyping, and production deployment of Generative AI/LLM solutions and the COE reference architecture (RAG, agents, model routing, evaluation, guardrails). Evaluate vendors, integrate GenAI into enterprise systems, mentor technical leads, and drive reusable patterns and responsible AI practices across hybrid cloud environments.

Design and execute ML research and end-to-end model development for generative and summary-generation systems. Lead rigorous experiments, translate findings for stakeholders, partner with engineers to deploy models, track state-of-the-art advances, and define roadmaps to improve promotional writing and discovery for members.

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.

Lead architecture and delivery of Generative AI solutions across Life & Annuities insurance, defining secure, scalable patterns (RAG, vectors, agents), integrating AI with policy administration, underwriting, and third-party systems, and enforcing governance, observability, and compliance while mentoring engineering teams and guiding cloud and data platform choices.

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 role involves developing and deploying large language models for healthcare, applying advanced ML techniques, and collaborating with interdisciplinary teams.

Join Truveta as an ML PhD Intern focusing on LLMs and Generative AI, collaborating on research to innovate healthcare solutions using machine learning.

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.

Review, evaluate, and annotate AI-generated text, images, audio, and video in Simplified Chinese. Perform quality checks, identify edge cases, participate in calibration, and provide feedback to improve datasets and annotation guidelines.