Top Tech Jobs & Startup Jobs in Seattle, WA

Own end-to-end product and model launches, including planning, messaging, landing pages, announcements, campaigns, and enablement. Develop positioning for Voice AI products, translate technical capabilities into business outcomes, maintain sales materials, track competitors, produce customer proof, and identify growth opportunities. Partner cross-functionally with product, research, sales, growth, customer success, and brand teams while independently writing and shipping marketing assets.

Own paid growth and account-based marketing programs targeting new and expansion accounts. Manage cross-platform retargeting, paid support for events, webinars, launches, direct mail, and geo-targeted activations. Optimize Google Ads and conversion channels, test emerging formats, manage six-figure monthly budgets, and measure account engagement, pipeline, revenue, and API activations. Partner with Sales, Revenue Operations, Events, Product Marketing, Brand Studio, and Analytics while using AI-enabled workflows to improve campaign execution and reporting.

Own full-cycle recruiting for 5–6 go-to-market roles, including sourcing, candidate assessment, interview coordination, offer management, and closing. Partner with hiring managers across Sales, Customer Solutions, and Marketing while maintaining accurate Greenhouse data and delivering a strong candidate experience. The role requires high-volume hiring, passive sourcing, knowledge of sales compensation, and effective use of recruiting technology and AI tools.

Own full-cycle recruiting for research and senior engineering roles, including ML researchers, research engineers, and ML engineers. Build outbound talent pipelines through frontier labs, academic groups, publications, open-source communities, and conferences. Partner with technical leaders on role definition and interview design, assess candidates, close senior hires against competing offers, manage candidate relationships, improve recruiting operations, and use data and AI tools to optimize sourcing and funnel reporting.

Develop and optimize large-scale deep learning training, data processing, and production inference systems. Maintain JAX and TPU training infrastructure, improve data quality and evaluation, translate research prototypes into production, optimize speech and language model inference, and resolve performance bottlenecks from kernels through system architecture. Collaborate across research, infrastructure, and production engineering while measuring impact and improving experimental velocity.