Top Tech Jobs & Startup Jobs

As a Verification Engineer Intern, you'll verify System on Chip designs, research verification practices, work on technical projects, and collaborate with mentors and teams.

Lead proactive investigations into Community Guidelines and Terms of Service violations, analyze trends using large datasets, propose detection or policy responses, launch and own workflows, coordinate with product/engineering and law enforcement, improve tooling and processes, and train other Trust & Safety team members. Must handle sensitive/disturbing content and support weekend/standing schedules.

Lead and grow a team of ML and software engineers to build large-scale indexing, retrieval, and ranking systems for Snapchat ads. Define architecture and evaluation frameworks, collaborate with product and data science, measure outcomes, and raise engineering and ML standards while shaping Snap's broader ML strategy.

Design, develop, and maintain a cross-platform, high-performance graphics rendering engine for Bitmoji. Build artist tools, profile and optimize performance, perform code reviews, evaluate technical tradeoffs, and deliver scalable, production-quality graphics features for millions of users.

As a Lead in Proactive Trust & Safety Operations, you'll investigate guideline violations, analyze trends, and improve processes while collaborating with cross-functional teams to enhance safety on Snapchat.

Lead end-to-end project management for Transparency Reporting, coordinate cross-functional partners, analyze metrics, draft and publish public reports, support regulatory compliance and audits, and improve reporting processes and automation.

Lead and grow a design team that defines the ad and monetization product experience for Snapchat. Set product design direction, collaborate with PMs, engineers, researchers and data scientists, improve enterprise design systems, represent design to leadership, and use research to inform advertiser, creator, and agency experiences.

Design, build, and optimize scalable ML infrastructure for training, evaluation, and high-performance inference. Develop feature generation/serving pipelines, data management and labeling systems, and collaborate with ML engineers to deploy production models while ensuring reliability, scalability, and production-quality code.