Staff Machine Learning Engineer (Research Scientist) - DFAI
Lead technical strategy and development of Plaid’s financial foundation models as a Staff ML Engineer. Own full ML lifecycle from pretraining and data curation to production serving, evaluation, and cross-team integration while mentoring engineers and setting technical standards.
About the job
Responsibilities
- Own 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.
- Drive research that ships: make decisions from experimentation through production systems that serve real customers and power multiple product teams.
- Work across the full ML stack, including pretraining objectives, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.
- Set technical direction and mentor a high-caliber team, with your work amplifying the capabilities of engineers and product teams across Plaid.
- Help 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.
Nice-to-Haves
- Fintech / financial data domain experience.
- External publications or open-source contributions.
- Experience defining ML platform capabilities (serving infra, feature stores) used across multiple teams.
Skills
Transformers, LLMs, Foundation Models, Distributed Training, Python, Large-Scale Training, Domain Adaptation, Model Serving, Model Monitoring, ML Infrastructure, Feature Stores
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