Staff Machine Learning Engineer - Fraud Data at Plaid

New York, New York, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

FintechIndustries

Requirements

8+ years total experience, with at least 5 years building and deploying production ML systems
Proven experience in machine learning infrastructure/operations
Demonstrated technical leadership and architectural vision, driving systems from concept to production
Proficiency in Python, PyTorch, Spark, SageMaker, and Airflow, or equivalent technologies
Nice to have - experience working in fraud detection, risk modeling, or financial security domains
Nice to have - background in graph machine learning or related techniques

Responsibilities

Design and build scalable ML infrastructure for Plaid’s fraud detection product
Work at a fast-paced environment to build a rapidly growing product with a championship team
Solve complex problems at the intersection of ML systems, data, and reliability
Build the foundations for fraud detection on the largest financial dataset in the world
Collaborate with talented engineers and data scientists across Plaid

Skills

Key technologies and capabilities for this role

Machine LearningML InfrastructureFeature PipelinesModel TrainingModel DeploymentMonitoringObservabilityScalable SystemsFraud Detection

Questions & Answers

Common questions about this position

What is the salary range for this Staff Machine Learning Engineer position?

The target base salary for this position ranges from $253,200 to $400,000 a year.

Is this role remote or does it require working from an office?

This information is not specified in the job description.

What skills and experience are required for this role?

Candidates need 8+ years total experience with at least 5 years building and deploying production ML systems, proven experience in machine learning infrastructure/operations, demonstrated technical leadership, and proficiency in Python, PyTorch, Spark, SageMaker, and Airflow or equivalents.

What is the team culture like at Plaid's Fraud Data team?

The team works in a fast-paced environment building a rapidly growing product with a championship team, collaborating with talented engineers and data scientists across Plaid, and owns the entire ML lifecycle.

What makes a strong candidate for this Staff Machine Learning Engineer role?

Strong candidates have 8+ years of experience including 5+ years in production ML systems, technical leadership in ML infrastructure, proficiency in key tools like Python and PyTorch, and nice-to-haves like fraud detection or graph ML experience; they should be ready to mentor and shape technical vision.

Plaid

Connects financial accounts to apps securely

About Plaid

Plaid simplifies financial data management for individuals and businesses by connecting various financial accounts to apps and services. Its main product is a set of APIs that allow developers to integrate financial data into their applications, enabling users to track spending, initiate payments, and access financial services all in one place. Plaid serves a wide range of clients, including app developers and financial institutions, and is used by popular apps like LendingTree and Square. Unlike many competitors, Plaid focuses on providing a comprehensive and scalable platform that supports various financial use cases, such as transactions and identity verification. The company's goal is to enhance the way users interact with their financial data, making it easier and more secure.

San Francisco, CaliforniaHeadquarters

2013Year Founded

$714.3MTotal Funding

SERIES_DCompany Stage

Fintech, Financial ServicesIndustries

1,001-5,000Employees

Benefits

We've got you covered: From medical, life, and 401ks, we’re here to support your physical, mental, and financial wellbeing.

Everyone is an owner: We want everyone to feel ownership over their work - literally, which is why we offer equity to full-time Plaids.

Vacation your way: We want to make sure you have time to meet your personal needs with unlimited PTO and two weeks of synchronous, company-wide vacation.

Grow your skills: Every Plaid is in control of their career development with our learning stipends, tools, and trainings.

Risks

Increased competition from API-based banking solutions like FIS's Code Connect platform.

Potential legal challenges, such as PNC's lawsuit over trademark issues.

Demand for enhanced transparency and security in financial data sharing.

Differentiation

Plaid offers seamless financial data integration through robust APIs for diverse clients.

Plaid's Pay by Bank for Bill Pay provides a cost-effective recurring payment solution.

Plaid's strategic partnerships enhance its value proposition in payroll and payment sectors.

Upsides

Plaid's expansion into the Triangle area indicates growth and increased hiring potential.

Partnership with Dwolla enhances Plaid's presence in the secure payments sector.

Collaboration with Ansa expands market reach through pay-by-bank capabilities for merchants.

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