Staff Machine Learning Engineer

Remitly•Seattle, WA

14h•Hybrid

About The Position

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 Staff 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. We are looking for a seasoned Machine Learning Engineer to set technical direction and build large scale systems that propel our next phase of growth.

Requirements

Degree in Computer Science, Data Science, Statistics, Mathematics or equivalent practical experience.
10+ 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).
Experience working with major cloud platforms (AWS, GCP, or Azure).
Demonstrated experience leading cross-functional ML initiatives and mentoring senior engineers.

Nice To Haves

Experience with one or more of Deep Learning algorithms, Large Language Models, Causal Inference, Personalization, Knowledge Graphs, Natural Language Processing would be desirable.
Experience with transformer architectures, pretraining, post-training, and fine-tuning of LLMs would be strongly preferred.

Responsibilities

Lead the end-to-end ML lifecycle from raw data to EDA to model training to offline/online evaluation and model 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.
Mentor and lead other engineers: champion design reviews, technical discussions, and maintain a high bar for code quality.
Drive technical strategy and influence ML architecture decisions across the organization.