Senior Software Engineer, ML Platform

About Us:

DailyPay is the leader in On-Demand Pay, helping employers modernize how people get their pay. DailyPay serves more than 1,900 employers and over 6 million employees, including many of the world's most recognized brands. By providing real-time access to earned pay and financial wellness tools, DailyPay helps employees manage their finances and helps employers attract and retain talent. DailyPay is helping define the future of pay, where money moves at the speed of work. Learn more at DailyPay's Press Center.

The Role

We are seeking a Senior Software Engineer to build DailyPay's ML platform from the ground up. You will design and build the infrastructure that every machine learning model at DailyPay runs on: feature engineering platform, model training and deployment, serving infrastructure, and the monitoring that keeps it all reliable in production.

This is a software engineering role, primarily working in Python, Scala, Go, and managing infra as code with Terraform. You will build the platform that data scientists use to ship models, not build the models themselves. You own the infrastructure that makes their work reproducible, testable, observable, and production-safe at scale.

You will work closely with data scientists, engineers, and product stakeholders to deliver high-quality ML solutions that directly impact DailyPay's core products. You are expected to operate with significant autonomy: defining work, identifying dependencies, and raising the bar for the team around you.

How You Will Make an Impact

• Platform Ownership: Help architect, design, and build DailyPay's unified ML platform from the ground up. Our ML Platform is a new unified system for model development, deployment, and monitoring that serves as the backbone for every AI and ML capability at the company.

• Systems Design & Delivery: Design and build scalable, reliable services and pipelines covering feature generation, model training, deployment, and inference. Own end-to-end delivery with minimal oversight.

• Self-Service Infrastructure: Build the tooling and guardrails that let data scientists define, test, and ship features and models independently, without needing an engineer in the loop and without bypassing validation, lineage, or rollback safeguards.

• Cloud Infrastructure: Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability. Experience in AWS SageMaker or GCP Vertex is ideal.

• CI/CD Pipeline Development: Build and maintain robust CI/CD pipelines for continuous integration and deployment of ML models and related infrastructure.

• Design monitoring and alerting systems for ML infrastructure and models using tools like Datadog. Proactively identify and resolve issues before they impact production.