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Senior Machine Learning Engineer - Infra/Ops - Fraud

New York City Office · Seattle Office · San Francisco HQ · New York, United States

Live, first seen by us 56 days ago
Pay
Pay not listedSimilar roles state $166K–$240K
Work setting
Hybrid — work setting source
Location type: Hybrid
From the employer’s posting
Employment
Full-time — employment source
Employment type: Full-time
From the employer’s posting
Team
All Departments › Data — team source
Department: All Departments › Data
From the employer’s posting

Before you apply

Sponsorship
Visa sponsorship not confirmed — sponsorship source
Will you now or in the future require Plaid to provide immigration-related support or sponsorship in order to employ you?
From the employer’s application form

What you’ll work on

Full posting
  • Develop high-performance feature computation and online inference pipelines

  • Build observability, monitoring, and automated debugging for ML systems

  • Use AI-assisted tools to investigate complex system behavior

  • Scale ML systems that power the fraud detection product

From the employer’s posting
own the development of high-performance feature computation and online inference pipelines
You’ll build robust observability, monitoring, and automated debugging capabilities
leveraging AI-assisted tools to investigate complex system behavior
Build and scale machine learning systems that power a rapidly growing fraud detection product

What you’ll bring

All qualifications

Core experience

  • 6+ years of relevant experience, with a strong focus on building, deploying, and scaling production machine learning systems.
  • Experience in fraud or risk domains.
  • Strong experience with ML infrastructure and operations, including production deployment, monitoring, and reliability.
  • Experience in Graph machine learning.
  • Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects.
  • Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow.
Qualification wording
6+ years of relevant experience, with a strong focus on building, deploying, and scaling production machine learning systems.
Experience in fraud or risk domains.
Strong experience with ML infrastructure and operations, including production deployment, monitoring, and reliability.
Experience in Graph machine learning.
Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects.
Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow.
Apply at Plaid

Posting record

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Status in our records
Live
First seen by us
Sep 14, 202656 days ago
Recorded sightings
77
Last seen by us
Nov 9, 2026
Employer says posted
Sep 12, 2026

These dates show when we found the posting. Check the employer’s website to confirm it is still accepting applications.

How this job compares

Pay

$166K–$240K is typical

The middle half of what similar US postings state. This one doesn’t list pay.

From 1,383 postings in the last year. Stated ranges, not salaries paid.

Time since we first saw it

Day 56

Similar jobs usually come down between day 7 and day 72.

Well past Plaid’s usual: its jobs come down by about day 16.

From 4,777 similar postings that came down, and 18 of Plaid’s. Coming down is not always a hire.

Plaid is hiring

16 open jobs

Open jobs by the week we first saw them, last 12 weeks.

  • Hiring at a steady pace
  • Takes jobs down at a usual pace
Hiring record

See how this role fits your experience

Add your resume and we’ll mark which of these tools you already use, and point out anything in the posting that could rule you out.

  • Python
  • SageMaker
  • Spark
  • Airflow
  • PyTorch
Where the posting names them
- Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects. - Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow. Nice-to-Have:

Your next step

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Demand for ML Engineer roles

New ML Engineer postings at the employers we follow are up 13% over the last 6 weeks compared with the 6 before.

Weekly new ML Engineer postings at the same employers, from the week of August 17 to the week of November 2. The fewest in a week was 618, the most 1,131, and the latest week had 1,131.
August 17Last week

Each bar is one week of new postings. The darkest bars are the last 6 weeks.

Counted from the hiring pages we’ve followed for at least five weeks, so employers we add don’t look like growth. Updated weekly.

About the role

We file this job under ML Engineer, by the work the posting describes.

ML engineers put machine learning models into products. They write the code that trains, ships, serves and monitors models so a recommendation or prediction works for real users.

Postings ask for strong Python and software engineering, a framework such as PyTorch, cloud services, and more and more often experience with large language models.

A data scientist often works out which model to build; an ML engineer makes it run reliably in production.

US government figures for computer and information research scientists

Median yearly pay, May 2025
$140,300
Expected change, 2025 to 2035
21.8% more jobsagainst 3.5% for all jobs

The government has no code for ML engineers, so these figures are for the closest group, computer and information research scientists.

Source: US Bureau of Labor Statistics, Occupational Employment and Wage Statistics (May 2025) and Employment Projections (2025 to 2035). Occupation code 15-1221.

Job description

View original posting ↗

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, SoFi, 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, Seattle, Washington D.C., Raleigh, London, and Amsterdam.

The Data team within Plaid’s Fraud organization builds the machine learning systems that power Plaid’s fraud detection products, leveraging Plaid’s unique network data to identify and stop fraud before it happens. The team owns the full ML lifecycle—from feature pipelines and model training to production serving and monitoring—building reliable, scalable systems that deliver high-quality fraud detection as we grow to support hundreds of customers.

As a Senior Machine Learning Engineer, you will own the development of high-performance feature computation and online inference pipelines that power production machine learning systems at scale. You’ll build robust observability, monitoring, and automated debugging capabilities, while leveraging AI-assisted tools to investigate complex system behavior and maintain high reliability. You’ll partner closely with ML Infrastructure, Data Science, and Product teams to execute critical technical initiatives and deliver scalable, high-impact ML solutions.

Responsibilities:

  • Build and scale machine learning systems that power a rapidly growing fraud detection product in a fast-paced environment.

  • Solve complex technical challenges at the intersection of machine learning, data infrastructure, and production reliability.

  • Develop foundational ML capabilities that leverage Plaid’s extensive financial network data to detect and prevent fraud.

  • Collaborate closely with engineers, data scientists, and cross-functional partners across Plaid to deliver high-impact solutions.

Qualifications:

  • 6+ years of relevant experience, with a strong focus on building, deploying, and scaling production machine learning systems.

  • Strong experience with ML infrastructure and operations, including production deployment, monitoring, and reliability.

  • Proven ability to independently own and deliver complex, end-to-end machine learning engineering projects.

  • Proficiency with Python and experience with ML and data technologies such as PyTorch, Spark, SageMaker, and Airflow.

Nice-to-Have:

  • Experience in fraud or risk domains.

  • Experience in Graph machine learning.

Our mission at Plaid is to unlock financial freedom for everyone. To support that mission, we seek to build a diverse team of driven individuals who care deeply about making the financial ecosystem more equitable. We recognize that strong qualifications can come from both prior work experiences and lived experiences. We encourage you to apply to a role even if your experience doesn't fully match the job description. We are always looking for team members that will bring something unique to Plaid!

Plaid is proud to be an equal opportunity employer and values diversity at our company. We do not discriminate based on race, color, national origin, ethnicity, religion or religious belief, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender, gender identity, gender expression, transgender status, sexual stereotypes, age, military or veteran status, disability, or other applicable legally protected characteristics. We also consider qualified applicants with criminal histories, consistent with applicable federal, state, and local laws. Plaid is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance with your application or interviews due to a disability, please let us know at accommodations@plaid.com.

Please review our Candidate Privacy Notice here.

Additional compensation in the form(s) of equity and/or commission are dependent on the position offered. Plaid provides a comprehensive benefit plan, including medical, dental, vision, and 401(k). Pay is based on factors such as (but not limited to) scope and responsibilities of the position, candidate's work experience and skillset, and location. Pay and benefits are subject to change at any time, consistent with the terms of any applicable compensation or benefit plans.

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Location & working pattern

New York, United States

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