Top AI Jobs in Seattle, WA

Lead the AI Platform team in building reliable, secure, observable infrastructure, shared services, deployment patterns, and operational practices for AI capabilities. Set technical priorities, coordinate cross-team dependencies, guide architecture and production operations, improve developer experience, and coach engineers while contributing to complex technical work.

Lead the technical strategy and multi-year roadmap for Data Labs, overseeing AI benchmarking systems, data pipelines, and reinforcement-learning environments. Manage engineering managers, forward-deployed engineers, and senior individual contributors while partnering with research, product, commercial, and executive teams. Guide platform investment, evaluation methodology, market positioning, client engagement, organizational growth, and architectural decisions supporting AI training and deployment.

Design, build, and maintain scalable full-stack applications; collaborate with cross-functional teams; integrate AI and agentic systems to enhance product features; ensure performance, fix bugs, and resolve bottlenecks.

Design and develop scalable software systems and production LLM solutions using RAG, agentic workflows, PEFT, and guardrails. Own architecture, technical execution, SDLC activities, cloud and Databricks governance, DevOps automation, testing, deployment, and system reliability. Collaborate with cross-functional teams, conduct code reviews, translate business requirements, and guide software design. The role is client-facing and follows a hybrid work model with travel as needed.

Leads the AI Solutions organization, including hiring, technical direction, intake, prioritization, staffing, architecture review, delivery, and production readiness. Converts internal AI requests into build plans, allocates virtual engineering and product teams, establishes standards for security, privacy, reliability, evaluation, and cost, and ensures successful handoffs. The role also develops reusable AI solution patterns and partners with executive, business, platform, architecture, privacy, and security stakeholders.

Design, develop, and implement AI/ML models; analyze large datasets; integrate AI solutions into existing systems; apply MavLink for autonomous systems; collaborate across teams and improve ML development processes.

Defines architecture and technical strategy for evaluating LLM, RAG, and agentic AI systems across the product lifecycle. Designs evaluation frameworks, metrics, datasets, dashboards, release gates, monitoring, and experimentation workflows. Analyzes model behavior, retrieval quality, failure modes, safety, and product impact, then guides improvements across prompts, tools, orchestration, search, UX, and code. Partners with engineering, applied science, data science, and product teams; leads cross-functional initiatives and mentors technical staff.

Lead the design, productionization, and operation of machine learning and agentic AI capabilities. Partner with product engineering, shape AI/ML architecture, build evaluation systems and data pipelines, and develop reliable MLOps tooling. Champion responsible AI, safety, latency, cost, and error handling. Serve as a technical resource for engineers, influence direction, mentor others, and occasionally explain AI functionality to customers or customer-facing teams.

Leads AI and data solution architecture for client transformation programs. Designs generative AI, data modernization, LLM, vector database, RAG, and LLMOps solutions; develops roadmaps and evaluation frameworks; advises executives; drives innovation; manages workstreams, client relationships, business growth, thought leadership, and team mentorship.

Leads engineering teams delivering scalable AI and machine learning solutions on Google Cloud. Oversees generative AI architecture, Vertex AI, LangChain, LangGraph, LLM APIs, RAG, experimentation, evaluation, guardrails, and LLMOps. Guides technical strategy, client alignment, delivery execution, and team development while applying Python, data science, and machine learning methods to build reliable AI products.

Leads Google Cloud AI and machine learning engineering teams and client workstreams. Oversees generative AI, LLM application, agent orchestration, retrieval-augmented generation, model evaluation, prompt engineering, guardrails, and LLMOps initiatives. Reviews architecture, code quality, testing, and solution delivery using Vertex AI, LangChain, LangGraph, Python, and LLM APIs. Coaches team members, improves reusable delivery assets, and manages client relationships, timelines, quality, and expectations.

Leads client-focused generative AI and data modernization architecture, translating business needs into scalable solutions using Google Cloud, Vertex AI, LLMs, vector databases, and modern data platforms. Advises stakeholders, develops AI evaluation and guardrail practices, guides experimentation and deployment, presents technical recommendations, supports proposals, and coaches teams through complex transformation programs.

Leads engineering for enterprise agentic AI and LLM platforms. Designs and deploys production-grade LLM services, RAG solutions, autonomous agents, APIs, and cloud-native AWS architectures. Provides technical leadership, architecture guidance, code reviews, MLOps, monitoring, governance, and operational excellence. Drives responsible AI practices and adoption of AI-assisted development across teams, emphasizing security, auditability, human oversight, scalability, and performance.

Build complete applications, workflow automations, and basic AI agents by directing AI coding agents. Translate customer needs into specifications, deploy full-stack solutions, instrument usage and outcomes, evaluate AI-generated work for security and reliability, engage with customers, and document effective prompts and development patterns.

Build complete applications, workflow automations, and basic AI agents using AI coding agents. Translate customer needs into specifications, deploy solutions, instrument usage and outcomes, evaluate AI-generated output for security and quality, engage with customers, and document effective prompts and development patterns.

SynX Therapeutics, a biotech start-up, invites speculative applications for future roles related to drug discovery and engineered biology.

Review, label, and QA datasets for AI/ML training; maintain trackers, databases, and dashboards; follow annotation guidelines and SOPs; prepare and clean data; document processes; collaborate to resolve data queries while ensuring confidentiality and timely delivery.

Lead the AI Quality engineering team while remaining hands-on building evaluation infrastructure, observability tooling, diagnostic systems, datasets, annotation workflows, and CI/CD pipelines for production AI agents. Run experiments across prompts, models, routing, caching, and agent harnesses to improve quality, latency, and cost. Partner with AI Core engineering and manage technical direction and team priorities.

Owns the design, deployment, governance, and operation of enterprise AI platforms and employee-facing agents. Builds secure control layers including identity, access, device, tenant, MCP, secrets, logging, rate limiting, and cost controls. Establishes AI lifecycle processes, evaluates and rolls out new tools, operates infrastructure to SRE standards, and develops telemetry, dashboards, runbooks, and incident response. Partners with Security, Legal, Privacy, and IT teams to translate governance requirements into enforceable technical controls.

Leads clinical intelligence initiatives by sizing AI opportunities, conducting data discovery, evaluating AI/ML solution performance, and measuring impact, accuracy, drift, and patient outcomes. Partners with product, clinical, engineering, compliance, and operations teams to develop measurement plans, value hypotheses, benchmarks, and scalable intelligence capabilities. The role is remote with occasional travel and requires advanced SQL, Python, clinical analytics, and population health research expertise.

Architect and build reliable, scalable AI platform services supporting model training and inference across hybrid-cloud environments. Develop reusable APIs, SDKs, tooling, and reference architectures; establish observability, resilience, security, automation, and cost-optimization standards. Partner with engineering and product teams to deliver platform capabilities, meet reliability targets, resolve production incidents, and drive remediation. Mentor engineers and promote responsible, secure use of AI-assisted development tools.

Designs, develops, and troubleshoots secure, scalable ML systems and services. Builds production software in Python using ML frameworks, cloud-native technologies, distributed systems, microservices, and NoSQL databases. Creates architecture artifacts, analyzes large datasets, improves system reliability and performance, and uses AI-assisted development tools responsibly. Preferred experience includes GPU workloads in Kubernetes, model-serving frameworks, distributed inference, model compression, and edge deployment.

Lead the design, development, and operation of an enterprise GenAI serving platform. Build high-throughput LLM inference, gateway, routing, GPU optimization, quantization, distributed serving, and observability capabilities. Establish reliability practices, SLOs, performance benchmarks, cost controls, and secure production standards. Drive adoption of agentic AI-enabled engineering workflows with governance, auditability, and human validation while influencing senior stakeholders and enabling reusable platform components across teams.

Design, build, and operate AI-enabled applications and ML/LLM platforms. Deliver production-quality code, SDK/REST integrations, MLOps pipelines, model/versioning and CI/CD, distributed fine-tuning and evaluation, and reusable platform components. Drive adoption of agentic AI workflows, enforce responsible AI and security controls, automate reliability, and influence senior stakeholders to align platform roadmaps with business outcomes.

Leads end-to-end applied AI and machine learning solutions for payment trust, safety, fraud prevention, and operational efficiency. Defines AI/ML strategy, data and evaluation plans, production deployment, model lifecycle practices, monitoring, and incident response. Partners with product, engineering, risk, compliance, and senior stakeholders while remaining hands-on with ML, NLP, document extraction, and cloud-based systems. Drives technical reviews, communicates tradeoffs, and ensures reliable, explainable, audit-ready solutions.