Machine Learning Engineer II

Posted Yesterday
Seattle, WA
In-Office
Mid level
eCommerce • Fintech • Payments • Software • Financial Services
We provide trusted digital financial services to our customers and recipients in over 170 countries across the globe.
The Role
As a Machine Learning Engineer II, you will design, implement, and maintain machine learning models, optimize workflows, and collaborate with cross-functional teams to deploy ML systems.
Summary Generated by Built In

Job Description:

About the Role:

At Remitly, we are on a mission to transform lives by providing trusted financial services that transcend borders. We serve millions of customers sending billions of dollars each year across 170+ countries, and our work impacts communities on a truly global scale.

As a Machine Learning Engineer in Remitly's Core AI/ML team, you'll work at the heart of our AI strategy. The Core AI/ML team is responsible for building the foundational machine learning systems, frameworks, and services that power Remitly's next generation of products and operations. We work horizontally across domains at Remitly—Global Money Movement, Treasury, Identity & Trust, Promotions, Referrals, etc.

You'll collaborate with experienced engineers and data scientists to design, develop, and deploy machine learning models that help scale Remitly's mission and impact worldwide.

You Will:

  • Implement and maintain ML models across the full lifecycle—from data exploration and feature engineering to training, evaluation, and deployment.

  • Design, build, and scale feature pipelines for batch and real-time ML applications.

  • Partner with data scientists, product owners, and engineers across verticals to turn prototypes into reliable, customer-facing ML systems.

  • Optimize models and pipelines using MLOps best practices: automated retraining, drift detection, CI/CD, monitoring, observability.

  • Contribute to technical discussions and design reviews to improve team practices and systems.

You Have:

  • Degree in Computer Science, Data Science, Statistics, Mathematics or equivalent practical experience.

  • 2+ years delivering Machine Learning systems in production.

  • Strong programming skills in Python, Go, Scala or a similar language.

  • Hands-on experience with ML frameworks (e.g., PyTorch, scikit-learn, numpy).

  • Familiarity working with major cloud platforms (AWS, GCP, or Azure).

  • Experience with one or more of Deep Learning algorithms, Large Language Models, Casual Inference, Personalization, Knowledge Graphs, Natural Language Processing would be desirable.

Remitly is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

Top Skills

AWS
Azure
GCP
Go
Numpy
Python
PyTorch
Scala
Scikit-Learn

What the Team is Saying

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The Company
HQ: Seattle, WA
2,800 Employees
Year Founded: 2011

What We Do

Since 2011, Remitly has been tirelessly delivering on our promises to our customers sending their hard earned money home. Today, we are incredibly proud to have served millions of customers globally. We strive daily to meet our promise to our customers by building peace of mind into everything we do. Join over 2,700 employees across 10 offices who are growing their careers while having a positive impact on people globally.

Why Work With Us

We are united through our vision to transform lives with trusted financial services that transcend borders. We accomplish our vision by relentlessly focusing on culture via our cultural values. That starts with putting customers at the center of everything we do, and includes investing in the growth & development of our team.

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Remitly Teams

Team
Product & Tech
About our Teams

Remitly Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

Our best work begins in moments when ideas spark, when we are deeply connected, and when we’re moving fast to solve challenging customer problems. These moments shape our culture. Our guidance is to committing to 3 days a week in the office.

Typical time on-site: 3 days a week
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