Whatnot

Software Engineer, Machine Learning Infrastructure at Whatnot, focusing on scaling AI and ML infrastructure for large language models and other ML applications. Responsibilities include owning AI/ML infrastructure, prototyping and productionizing ML architectures, designing and scaling inference infrastructure for low-latency and high-throughput serving, and building distributed training and inference pipelines.

Software Engineer, Machine Learning Infrastructure at Whatnot, focusing on building and scaling the core infrastructure for AI and ML models, including low-latency large model serving and distributed training/inference pipelines.

Machine Learning Platform Engineer at Whatnot, focusing on building and scaling the core infrastructure for AI and ML models, including LLM applications, low-latency serving, distributed training, and GPU inference.

Seeking an LLM Platform Engineer to design and scale the core infrastructure for large language model applications at Whatnot. This role involves building systems for retrieval, grounding LLM responses, developing evaluation frameworks, and implementing feedback pipelines to bring AI into production for various business surfaces like growth, recommendations, trust and safety, and fraud.

Lead the development and scaling of core ML infrastructure, including low-latency model serving, streaming feature ingestion, distributed training, and high-throughput GPU inference, to power AI/ML applications at consumer scale. This role involves hands-on coding, architectural guidance, and empowering ML scientists.

Seeking an LLM Platform Engineer to build and scale the core infrastructure for large language model applications at Whatnot. This role involves designing and deploying RAG systems, developing LLM evaluation frameworks, and creating human-in-the-loop feedback pipelines to integrate AI into critical business functions like growth, recommendations, trust and safety, and fraud.

Seeking an ML Infrastructure Engineer to design and scale core infrastructure for ML and LLM applications, focusing on low-latency serving, distributed training, and high-throughput GPU inference to productionize cutting-edge models.

Machine Learning Engineer focused on fraud detection and prevention for a livestream shopping platform. The role involves designing, training, and deploying ML models (traditional and LLM-powered), building intelligent user graphs, developing data pipelines and real-time inference systems, conducting data analysis, and implementing model monitoring. Collaboration with cross-functional teams and staying ahead of emerging fraud tactics are key.

Senior Engineering Manager, ML Platform at Whatnot, a livestream shopping platform. This role focuses on leading the development and scaling of core infrastructure for machine learning and self-hosted LLM applications. Responsibilities include building low-latency model serving, streaming feature ingestion, distributed training, and high-throughput GPU inference systems. The role requires strong technical depth, hands-on coding, and managing production ML systems at consumer scale.

Machine Learning Engineer focused on driving top-of-funnel growth and first-time buyer success for a livestream shopping platform. This role will build and own ML models for personalization in onboarding and user targeting, impacting buyer growth and first-time user conversion.

Machine Learning Engineer focused on driving top-of-funnel growth and first-time buyer success for a livestream shopping platform. This role will build and own ML models for personalization in onboarding and user targeting, impacting buyer growth and first-time user conversion.

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.

This role focuses on building and scaling the feature ingestion and storage infrastructure that powers both core business logic and ML applications at Whatnot. The engineer will work with ML scientists to leverage near-real-time signals for critical business surfaces like growth, recommendations, trust and safety, and fraud. Key responsibilities include designing and evolving real-time feature pipelines for online and offline stores, optimizing system performance, and empowering scientists with abstractions and tools for near-real-time features. The role requires experience with production ML systems, software engineering for consumer-scale loads, Python, and various databases and cloud platforms.

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.