채용
필수 스킬
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
총 조회수
1
총 지원 클릭 수
0
모의 지원자 수
0
스크랩
0
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Plaid 소개

Plaid
Series DThe easiest way for users to connect their bank accounts to an app.
501-1,000
직원 수
San Francisco
본사 위치
$13.4B
기업 가치
리뷰
3.1
5개 리뷰
워라밸
2.5
보상
4.0
문화
2.8
커리어
2.9
경영진
2.7
45%
친구에게 추천
장점
Competitive compensation package
Good benefits offerings
Location flexibility options
단점
Concerns about long-term career growth
Limited mentorship compared to other companies
Toxic attitudes in tech community
연봉 정보
269개 데이터
Mid/L4
Senior/L5
Director
Mid/L4 · Data Scientist
10개 리포트
$175,713
총 연봉
기본급
$143,960
주식
$31,753
보너스
-
$119,175
$263,794
면접 경험
3개 면접
난이도
3.0
/ 5
소요 기간
21-35주
면접 과정
1
Application Review
2
Recruiter Screen
3
Technical Phone Screen
4
Onsite/Virtual Interviews
5
Background Check
6
Offer
자주 나오는 질문
Coding/Algorithm
Technical Knowledge
Behavioral/STAR
System Design
Past Experience
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