Top Tech Jobs & Startup Jobs in Seattle, WA

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Build and optimize Snap’s machine learning infrastructure, including scalable training, evaluation, inference, feature serving, data management, vector search, and model deployment systems. Improve reliability, performance, and cost efficiency for large-scale ML workloads while collaborating with ML engineers. The role requires strong programming, distributed systems, big data, cloud infrastructure, and production machine learning experience.

Leads business intelligence and analytics engineering initiatives for Snap’s People Team. Designs scalable data pipelines, models, data products, dashboards, and self-service analytics solutions. Translates business needs into technical requirements, conducts statistical analysis, improves data quality and operational reliability, and delivers actionable insights. Owns systems end to end while mentoring team members and communicating effectively with executives, business partners, analysts, and technical teams.

Design and scale technical systems supporting advertiser acquisition, activation, retention, and growth. Build orchestration layers integrating CRM, BigQuery, Ads APIs, experimentation infrastructure, personalization, communications, signals, and automation tools. Translate GTM objectives into scalable systems and measurable outcomes while partnering across Sales, Growth, BI, Finance, Product, and Engineering. Recruit and lead technically fluent teams, manage roadmaps, and communicate adoption, ROI, and business impact to executives.

Build and ship automation systems powering Snap’s long-tail advertiser growth across acquisition, activation, retention, and expansion. Integrate CRM, BigQuery, Ads APIs, and other data platforms; develop personalized recommendations and multichannel outreach; automate workflows; and use behavioral and performance data for decisioning. Partner cross-functionally with Product, Engineering, Sales, Finance, and Business Intelligence, while mentoring peers and communicating outcomes to leadership.

Build and deploy automated systems supporting Snap’s long-tail advertiser growth across acquisition, activation, retention, and expansion. Integrate CRM, BigQuery, GCP, Ads APIs, and data platforms to create unified account context, personalization workflows, multi-channel outreach, and decisioning tools. Partner across product, engineering, and sales while driving experimentation, operational efficiency, reusable APIs, and measurable revenue or cost-saving outcomes.

Design and implement logging and alerting systems across endpoints, infrastructure, networks, and cloud environments. Build automation for threat hunting and incident response, investigate and triage security events, lead incident response efforts, and develop threat intelligence on advanced adversary tactics. The role also requires secure, scalable software development using Python or Go and expertise across operating systems, Kubernetes, and cloud platforms.

Leads a team of Client Partners responsible for account growth, client and agency relationships, revenue quotas, KPI-driven measurement strategies, and vertical growth plans. Coaches and develops direct reports, supports quota and account assignments, translates market trends into business opportunities, and drives the sales process from planning through close. The role requires senior-level digital media sales experience, people management, strong client-facing communication, and collaboration across functions.

Build privacy controls for advertising, measurement, machine-learning, and distributed systems. Conduct privacy reviews, threat modeling, and risk assessments; evaluate privacy-preserving techniques and attack risks; partner with engineering, product, legal, security, and data teams; and develop scalable privacy standards, auditing practices, tools, and documentation.

Leads large-scale, ambiguous ML platform and infrastructure programs across engineering organizations. Owns program lifecycles, dependencies, roadmaps, technical decisions, reliability, performance, cost optimization, and operational excellence. Uses Python, SQL, dashboards, and automation to analyze systems and guide decisions. Partners with senior engineering and product leaders, drives accountability, mentors junior TPMs and engineers, and improves platform-wide systems and engineering velocity.