Top Tech Jobs & Startup Jobs in Seattle, WA

Own LLM inference optimization across production workloads, including benchmarking, quantization, speculative decoding, GPU efficiency, inference engine evaluations, KV-cache optimization, and chaos engineering. Partner with platform teams on large-scale AWS GPU serving architectures, establish performance standards, and communicate technical trade-offs to senior leadership. Lead secure, governed adoption of agentic AI engineering workflows and automation across teams.

Lead design and adoption of cloud-based, portable AI platform architectures across hybrid environments. Advise on technology selection, document target-state designs, shepherd architecture reviews, develop multi-year roadmaps, integrate enterprise AI capabilities, produce secure production code, and align engineering with architectural governance and controls.

Lead design, build, and operate scalable ML training platform on cloud. Optimize GPU single-node and distributed training, manage Kubernetes-based infrastructure, enable GenAI/LLM fine-tuning workflows, implement observability, improve developer CI/CD and automation, and enforce governance, security, and responsible AI practices.

Lead design and build secure, resilient cloud-native distributed systems; write Java/Python production code (Spring); perform threat modeling, vulnerability remediation, incident response, and ensure audit-defensible security controls and SDLC practices. Mentor engineers and validate AI-assisted security outputs.

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.

Build software, automation, observability, and telemetry systems for a large-scale global enterprise network. Define and measure reliability targets, replace manual operational processes with code, improve incident detection and recovery, support a resilient global DMZ, and codify disaster recovery across regions.

Leads software engineering for secure, scalable AI/ML data platform services. Responsibilities include designing, coding, reviewing, testing, debugging, and operating 24x7 tier-one services; guiding engineers and vendors; driving responsible AI-assisted development practices; establishing validation and security standards; influencing architecture and technical operations; and contributing as a firmwide engineering subject matter expert.

Leads software engineering strategy and development across multiple products and teams. Builds reusable frameworks, architects secure agentic AI engineering workflows, governs responsible AI practices, and operates 24x7 tier-one online services. Translates complex technical issues for leadership, drives adoption of modern development methods, influences senior stakeholders, and delivers scalable cloud-native solutions.

Lead design and build of a global AI/ML deployment platform: architect control plane, multi-region deployments, APIs, autoscaling, and integrations. Own technical direction, engage clients, drive governance of AI-assisted development, and ensure resilient, secure production-grade infrastructure.

Designs, develops, experiments with, and productionizes machine learning, deep learning, and generative AI solutions. Builds LLM and agentic workflows, conducts model tuning and evaluation, optimizes system performance, and develops end-to-end Python solutions. Partners with product and engineering teams to integrate generative AI into scalable ML platforms and production services, with a focus on NLP, RAG, embeddings, inferencing, and MLOps.