Software Engineer focused on building AI development tools and infrastructure to enhance engineering productivity and enable autonomous agent capabilities within the company's codebase and processes. This role involves designing sandboxed environments, orchestration systems, and AI-aware infrastructure, acting as a primary interface between the Developer Tools team and the AI Tooling Working Group.

Software Engineer to build and own AI tooling for the engineering organization, focusing on AI coding agents and assistants. This role involves building infrastructure, workflows, and orchestration systems for AI agents to operate within the company's codebase and processes, enabling both engineers and non-engineers to leverage AI tools effectively. The role requires strong software engineering skills and familiarity with current AI development tools.

Machine Learning Engineer role focused on building and deploying ML models for personalized navigation, search, and recommendations on a livestream shopping platform. The role involves end-to-end ML project lifecycle, from data to production, and requires experience in applied ML fields like search and recommendations.

Machine Learning Engineer focused on designing, training, and deploying ML models and systems for fraud detection and prevention in a high-volume marketplace. This includes building user graphs, real-time inference systems, and monitoring, with a strong emphasis on end-to-end ownership and integrating ML into fraud orchestration.

Machine Learning Engineer on the Discovery Content and Navigation team, focusing on building and deploying ML models for personalized navigation, search, and recommendations in a high-growth consumer marketplace. The role involves end-to-end project ownership, from data collection to production deployment and experimentation, with a strong emphasis on applied ML methods for consumer-scale data.

Software Engineer role focused on building and scaling the Discovery Platform, including recommendation systems, feed, browse, and search functionalities. This involves integrating retrieval, machine learning ranking, real-time processing, and content understanding to create a personalized discovery experience for a live shopping marketplace.

Software Engineer role focused on building and deploying ML-driven systems for fraud detection, prevention, and intervention in a real-time, high-volume marketplace. Responsibilities include developing intelligent user graphs, training and deploying traditional ML and LLM models, creating scalable data pipelines, and implementing human-in-the-loop systems to adapt to evolving fraud tactics.

Software Engineer for Whatnot's Trust & Risk team, focusing on building and iterating on production algorithms and infrastructures to mitigate systemic risks, safeguard users, and prevent abuse. The role involves designing event-driven pipelines for real-time threat response, balancing risk with user experience, and collaborating across teams to continuously evolve defenses against emerging abuse patterns. Experience with machine learning, behavioral analysis, and data warehouses is preferred.

Software Engineer role focused on building systems for trust, risk, fraud, and integrity within a consumer marketplace. The role involves designing and developing production algorithms and infrastructures to mitigate systemic risks, protect users, and prevent abuse. It requires experience in event-driven pipelines, detection frameworks, and a data-driven mindset, with a preference for experience in Trust and Risk or Fraud domains. Explicit data science or machine learning experience is a plus.

Software Engineer role focused on building and deploying ML-driven systems for fraud detection, prevention, and intervention in a livestream shopping marketplace. Responsibilities include developing intelligent user graphs, training and deploying traditional ML and LLM models, creating scalable data pipelines, real-time inference systems, and human-in-the-loop systems for continuous refinement. The role emphasizes analyzing behavioral and adversarial data to identify emerging fraud trends and evolving systems to combat them.