Currently tracking 995 active AI roles, up 64% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $65k–$465k (avg $196k).
| Title | Stage | AI score |
|---|---|---|
| Principal Applied Scientist, Ring AI Principal Applied Scientist role focused on computer vision and multimodal LLMs, involving research, algorithm development, and translating research into practice for consumer products. Requires PhD, 10+ years of ML experience, and expertise in computer vision, VLM, and deep learning. The role involves defining research directions, developing long-term strategies, and mentoring junior scientists. | Post-trainAgent | 9 |
| Applied Scientist, Sales AI This role focuses on building AI/ML solutions for the Ad Sales business, specifically creating customer-facing recommendations and enhancing end-to-end workflows with Generative AI. The scientist will leverage quantitative modeling techniques like Sequential Recommender Systems, Deep Learning, and Reinforcement Learning, and use NLP and Generative AI for explainability. The role involves research, model development, A/B testing, and collaboration with engineering and product teams to deliver production-ready solutions. |
| AgentPost-train |
| 8 |
| Economist I, Promise Economist role focused on leading research, econometric modeling, and analysis to understand customer preferences and inform business operations. This involves developing analytic tools and economic models, including generative AI agents, to determine customer promise by considering inventory, fulfillment, carrier capabilities, customer preferences, and economic impacts. The role requires designing, building, and validating GenAI agents against traditional econometric models and production experiments, with the goal of driving changes in transportation and fulfillment networks. | Agent | 7 |
| Applied Scientist, Sales AI Applied Scientist role focused on Generative AI and quantitative modeling for Amazon Advertising Sales. The role involves conceptualizing and leading research on ML/GenAI solutions, guiding technical approaches, conducting data analysis, running A/B experiments, and working with engineers to deliver end-to-end solutions into production. Key areas include optimizing sales business, improving work efficiency through GenAI, and developing advertiser insights and recommendations. | Ship | 7 |