Currently tracking 20 active AI roles, down 31% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $245k–$345k (avg $296k).
Whatnot has 33 active AI-related job listings, with a significant focus on agents, which accounts for 64% of their open roles. The majority of these positions are within the Engineering function, and all hiring is currently concentrated in the United States. Their technical needs are reflected in frequent tags such as model_serving, inference_infra, and agent_orchestration. In the last 30 days, Whatnot posted 2 new AI roles, representing a 78% decrease compared to the previous 30-day period.
Whatnot currently has 9 active AI-related roles in our index. The most common open titles are: AI Tooling Engineer, Data Scientist, Risk & Fraud, Engineering Manager, Feed, Machine Learning Engineer, Growth, Software Engineer, AI Dev Tools. Most positions are in Engineering.
Whatnot's active AI hiring is concentrated in: agents (89%), application (11%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Whatnot is hiring AI talent in: United States (9 roles).
Job postings at Whatnot most frequently reference: agent orchestration, recommender systems, search ranking, model serving, rag.
In the past 30 days, Whatnot has posted 2 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Software Engineer, Machine Learning Infrastructure 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. | ServePost-train | 8 |
| Software Engineer, Machine Learning Infrastructure 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. |
| 8 |
| Machine Learning Platform Engineer 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. | ServePost-train | 8 |
| Technical Lead Manager, ML Infrastructure 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. | ServeData | 8 |
| Machine Learning Infrastructure Engineer 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. | ServePost-train | 8 |
| Senior Engineering Manager, ML Platform 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. | ServeData | 8 |
| Feature Platform Engineer This role focuses on building and scaling the feature ingestion and storage infrastructure that powers both core business logic and ML applications. The engineer will work on real-time feature pipelines, optimize system performance, and empower ML scientists to iterate faster by building abstractions and tools. The goal is to enable faster ML model responses to marketplace dynamics and scale AI across the ecosystem. | Serve | 5 |