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.
Currently tracking 10 active AI roles, down 27% versus the prior 4 weeks. Primary focus: Agent · Engineering.
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 - Embedded Insights Machine Learning Engineer focused on building and deploying AI/ML models into customer-facing financial products, driving initiatives from concept to production, and ensuring model performance and scalability. | ShipAgent | 7 |
| Senior Data Scientist - Embedded Insights Senior Data Scientist role at Plaid focused on building machine learning models for internal decision making and customer-facing products. The role involves analyzing network data, developing metrics, creating dashboards, evaluating model performance, and designing experiments to improve products and expand offerings within the fintech domain. Requires strong SQL, Python, and understanding of ML techniques. | Ship |
| 5 |