Plaid
Fintech · Financial data API
- HQ
- San Francisco, US
- Founded
- 2013
- Website
- plaid.com
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 |
|---|---|---|
| Senior Machine Learning Engineer - Credit Senior Machine Learning Engineer at Plaid focused on credit products. The role involves designing, building, and deploying scalable ML solutions and systems, experimenting with new modeling techniques, and owning the full model lifecycle from training to serving and monitoring. Collaboration with cross-functional teams to define the ML roadmap is also a key aspect. | ServePost-train | 7 |
| Staff Product Manager - AI Foundations Staff Product Manager for Plaid's AI Foundations team, responsible for defining the strategy and roadmap for the core AI and data layer. This role involves building scalable AI systems from embeddings and representation learning to applied model integrations, partnering with Engineering and Data Science to drive initiatives from concept to production, and ensuring responsible AI practices. | Serve |
| 7 |