As a Staff Machine Learning Engineer, you will lead the technical strategy and development of Plaid's foundation models, driving key decisions across pretraining objectives, model architecture, and fine-tuning approaches that power a wide range of downstream product applications. You will serve as the technical lead for the full machine learning lifecycle, overseeing everything from data curation and experimentation to production deployment, feature management, and observability. In this role, you will establish rigorous evaluation frameworks to measure model performance across diverse use cases and build scalable, repeatable pipelines that translate research into production impact. You will also partner closely with teams across the organization to define how products integrate with and adapt foundation models, enabling reusable ML infrastructure and reducing duplicated modeling efforts. As a senior technical leader, you will mentor engineers across experience levels, elevate engineering and experimentation standards, and communicate technical advancements both internally and externally as a representative of Plaid's AI and machine learning capabilities. Responsibilities: Owning the end-to-end technical strategy for a foundation model built on one of the world's richest financial datasets, from pretraining architecture to production serving. Doing research that ships: driving decisions from experimentation through production systems that serve real customers and power multiple product teams. Working across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration. Setting technical direction and mentoring a high-caliber team, with your work amplifying the capabilities of engineers and product teams across Plaid. Helping hundreds of millions of consumers achieve greater financial freedom through the ML capabilities you build and ship. Qualifications: MS: 7-12+ years of industry experience with a demonstrated track record of technical leadership and production delivery. PhD: 5-9+ years of industry experience with evidence of technical leadership (tech lead, principal/staff-equivalent roles) and end-to-end production ownership. Prior technical leadership experience (tech lead, principal, or staff) with demonstrated cross-team influence and mentorship. Deep expertise in Transformers/LLMs/Foundation Models, including large-scale training or domain adaptation. End-to-end production ownership; proven track record shipping models through training, serving, monitoring, and iteration in live environments. Distributed training experience and strong Python + software engineering fundamentals at a staff level. Ability to drive technical alignment across teams: setting standards, defining integration patterns, and influencing beyond your immediate scope. Fintech / financial data domain experience - Nice to have External publications or open-source contributions - Nice to have Experience defining ML platform capabilities (serving infra, feature stores) used across multiple teams. - Nice to have Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid! Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.