Staff Machine Learning Engineer (Research Scientist) - DFAI

Plaid operates a financial data network and API platform that lets businesses connect to financial institutions and build financial experiences. Its products support account and identity verification, real-time balance and transaction data, investment and liability data, income and underwriting workflows, fraud and AML risk checks, and multi-rail bank payments. It serves developers, businesses, financial institutions, platforms, lenders, banks, and consumer-facing financial-product providers.

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Skills

Data Curation Distributed Training Fine-Tuning Foundation Models Large Language Models Machine-Learning Mentorship Ml Infrastructure Model Architecture Model Evaluation Model Monitoring Model Pretraining Model Serving Python Transformers

Candidate Availability

Remote · Germany · Required

Required and preferred rules are kept separate and reflect the wording in the original posting.

About the Role

Lead the technical strategy and development of Plaid's foundation models across pretraining, architecture, fine-tuning, data curation, experimentation, production deployment, feature management, evaluation, and observability. Partner across teams to build reusable ML infrastructure and communicate technical advancements.

Requirements

MS with 7–12+ years of industry experience in technical leadership and production delivery, or PhD with 5–9+ years and evidence of technical leadership and production ownership.
Prior technical leadership experience as a tech lead, principal, or staff engineer, with cross-team influence and mentorship.
Deep expertise in Transformers, LLMs, and foundation models, including large-scale training or domain adaptation.
End-to-end ownership of models through training, serving, monitoring, and iteration in live environments.
Distributed training experience and strong Python and staff-level software engineering fundamentals.
Ability to drive technical alignment, set standards, define integration patterns, and influence beyond an immediate team.
Fintech or financial data experience is nice to have.
External publications or open-source contributions are nice to have.
Experience defining ML platform capabilities such as serving infrastructure and feature stores is nice to have.

Responsibilities

Own end-to-end technical strategy for a foundation model, from pretraining architecture through production serving.
Drive research decisions from experimentation into production systems serving customers and product teams.
Work across pretraining, architecture design, distributed training, serving infrastructure, monitoring, and cross-team integration.
Set technical direction and mentor engineers and product teams across Plaid.
Build and ship ML capabilities that support financial freedom for consumers.

Benefits