Currently tracking 10 active AI roles, down 27% versus the prior 4 weeks. Primary focus: Agent · Engineering.
Plaid currently has 14 active AI-related job listings. The majority of these roles, 43%, are focused on agents, with serving infrastructure also representing a significant portion at 21%. Engineering is the most frequent function, with all 14 roles located in the United States. Frequent tech tags include model_serving, inference_infra, and agent_orchestration, suggesting a focus on deploying and managing AI models.
Plaid currently has 16 active AI-related roles in our index. The most common open titles are: AI Marketing Technologist Lead, Analytics Engineer, Engineering Manager - Security, Engineering Manager, AI Applications, Fraud Researcher. Most positions are in Engineering and Research.
Plaid's active AI hiring is concentrated in: agents (44%), application (19%), pre-training (19%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.
Plaid is hiring AI talent in: United States (16 roles).
Job postings at Plaid most frequently reference: model serving, fine tuning, llm observability, agent orchestration, inference infra.
In the past 30 days, Plaid has posted 7 new AI-related roles.
| Title | Stage | AI score |
|---|---|---|
| Machine Learning Engineer (Research Scientist) - DFAI Research Scientist role focused on advancing Plaid's foundation models by developing novel architectures, pretraining objectives, and fine-tuning strategies. The role involves working across the full ML stack from data and feature engineering to training pipelines, model serving, and monitoring, with a strong emphasis on shipping research into production systems for financial applications. | PretrainServe | 9 |
| Senior Machine Learning Engineer (Research Scientist) - DFAI Lead applied research for Plaid's foundation model, focusing on architecture, pretraining, and fine-tuning for financial datasets. Build and maintain end-to-end ML systems, including training pipelines, model serving, and evaluation frameworks. Collaborate with product teams to adapt models and communicate research findings. |
| PretrainServe |
| 9 |