Currently tracking 194 active AI roles, up 94% versus the prior 4 weeks. Primary focus: Agent · Engineering. Salary range $120k–$487k (avg $234k).
| Title | Stage | AI score |
|---|---|---|
| AI/ML Software Engineer - SES Gen AI Solutions, IS&T AI/ML Engineer to design, develop, and deploy intelligent solutions for modern contact center platforms, focusing on scalable AI systems for chatbots, voice assistants, speech analytics, and automated customer support workflows. The role involves building end-to-end AI pipelines, including model development, deployment, and optimization, with deep expertise in LLMs, conversational AI, and real-time inference systems. | AgentServe | 8 |
| AI/Machine Learning Engineer AI/ML Engineer to build intelligent systems and deploy state-of-the-art AI models and systems across Apple's business groups. Responsibilities include implementing ML infrastructure, developing feature engineering and fine-tuning frameworks, designing ML pipelines, and optimizing models. The role requires designing systems from raw data to autonomous action, implementing RAG pipelines, working with embeddings and vector databases, building AI agents with tool use, and fine-tuning transformer models. Experience with large-scale data and ML frameworks is essential. |
| AgentData |
| 8 |
| Senior Machine Learning Engineer, Search & AI Senior Machine Learning Engineer focused on developing next-generation Search and Question Answering systems using cutting-edge search technologies and large language models. The role involves improving Query Understanding, Retrieval, and Ranking, leveraging fine-tuning, reinforcement learning, embeddings, deep learning, and online learning. Experience with RAG, retrieval, and generative LLMs is crucial. | AgentServe | 8 |
| Data Scientist This role focuses on building and scaling automated insight pipelines for the sales organization, developing ML models for opportunity detection and performance diagnosis, and embedding these insights into AI agents, dashboards, and GenAI tools. The role involves end-to-end insight development, including data preparation, statistical analysis, LLM prompt engineering, and deploying ML models for forecasting, anomaly detection, attribution, and causal inference. It also includes building RCA and recommendation engines, analyzing agent interactions, implementing LLM evaluation pipelines, and supporting experimentation. The role requires partnering with AI engineers and PMs, acting as a data translator, influencing upstream data model design, and driving KPI definitions. | AgentEval Gate | 7 |