招聘
必备技能
Machine Learning
Python
Feature Engineering
Model Deployment
Backend Development
We believe that the way people interact with their finances will drastically improve in the next few years. We’re dedicated to empowering this transformation by building the tools and experiences that thousands of developers use to create their own products. Plaid powers the tools millions of people rely on to live a healthier financial life. We work with thousands of companies like Venmo, So Fi, several of the Fortune 500, and many of the largest banks to make it easy for people to connect their financial accounts to the apps and services they want to use. Plaid’s network covers 12,000 financial institutions across the US, Canada, UK and Europe. Founded in 2013, the company is headquartered in San Francisco with offices in New York, Washington D.C., London and Amsterdam.
The Network Enablement team’s mission is to amplify Plaid’s network effects by fostering trust and sharing intelligence with data partners.
We build Trust & Fraud Insights (real-time Protect model scoring, two-way APIs/webhooks, and investigation tooling), Bank Intelligence (ML driven retention and account-primacy metrics and scalable batch pipelines), and the ml/data foundations (graph and sequence-embedding models plus unified feature pipelines and feature-store patterns).
We own productionization and reliability for data partner facing ML — low-latency scoring, offline↔online parity, observability and drift detection, PII-safe handling and auditability — and collaborated closely with MLE, DS, Data Platform, Fraud, Foundational Modeling, Product, and Privacy to scale network intelligence.
On this team you will build and operate the ML infrastructure and product services that enable trust and intelligence across Plaid’s network. You’ll own feature engineering, offline training and batch scoring, online feature serving, and real-time inference so model outputs directly power partner-facing fraud & trust products and bank intelligence features. You will integrate inference into product logic (APIs, feature flags, backend flows), build reproducible pipelines and model CI/CD, and ensure observability, reproducibility, and compliance as you scale our network capabilities. You’ll partner with Product, ML/Data Platform, Fraud, Foundational Modeling, MLE, DS, and Privacy to ship auditable, reliable ML solutions that move product KPIs
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关于Plaid

Plaid
Series DThe easiest way for users to connect their bank accounts to an app.
501-1,000
员工数
San Francisco
总部位置
$13.4B
企业估值
评价
3.1
5条评价
工作生活平衡
2.5
薪酬
4.0
企业文化
2.8
职业发展
2.9
管理层
2.7
45%
推荐给朋友
优点
Competitive compensation package
Good benefits offerings
Location flexibility options
缺点
Concerns about long-term career growth
Limited mentorship compared to other companies
Toxic attitudes in tech community
薪资范围
269个数据点
Mid/L4
Senior/L5
Director
Mid/L4 · Data Scientist
10份报告
$175,713
年薪总额
基本工资
$143,960
股票
$31,753
奖金
-
$119,175
$263,794
面试经验
3次面试
难度
3.0
/ 5
时长
21-35周
面试流程
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Background Check
6
Offer
常见问题
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
System Design
Past Experience
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