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 (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 |
| Staff Machine Learning Engineer (Research Scientist) - DFAI Staff Machine Learning Engineer (Research Scientist) at Plaid, focusing on the Data Foundation & AI team. The role involves leading technical strategy and development of foundation models, covering the full ML lifecycle from pretraining to production serving, evaluation, and monitoring. The position requires expertise in Transformers/LLMs, large-scale training, and distributed training, with a focus on shipping models that power product applications and mentoring other engineers. | PretrainServe | 9 |
| Engineering Manager, AI Applications Engineering Manager to lead a newly formed team focused on scaling Plaid's AI-powered customer experience and integrating Plaid into AI applications. The role involves managing a team, defining strategy, and driving execution on projects related to AI customer support agents, agentic commerce, and AI provider integrations. | AgentPost-train | 8 |
| Staff Software Engineer - Instant Access Staff Software Engineer on the Instant Access team, building applied AI systems based on LLMs that operate autonomously in production. The role focuses on creating, repairing, and improving financial institution integrations, with responsibilities including defining technical direction for autonomous AI systems, building LLM-powered code generation pipelines, designing evaluation frameworks for non-deterministic systems, and architecting self-healing and multi-layer safety architectures. | AgentEval Gate | 8 |
| Staff Software Engineer - AI Applications Staff Software Engineer role focused on building and scaling AI applications within Plaid's FinTech ecosystem. The role involves developing integration patterns for AI providers, enhancing conversational AI interfaces, architecting trust layers for agentic commerce, and extending AI-powered customer experience agents. This includes working with multi-turn, multi-agent systems, RLHF, and customer-specific memory, as well as expanding agents to support product recommendation, onboarding, and upselling. | AgentShip | 8 |
| 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 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 |
| Fraud Researcher Senior Fraud Researcher at Plaid, focusing on using financial network data, transaction patterns, and device signals to detect and prevent fraud. The role involves leading investigations, translating findings into detection improvements, and collaborating with Data Science, ML/AI, and Product teams to shape fraud capabilities. It emphasizes applied research, signal utilization, and ecosystem monitoring to stay ahead of adversaries, directly driving features, model inputs, and product design. | Ship | 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. | ServeAgent | 7 |