About this role
The team at DailyPay is focused on building and establishing the core Machine Learning platform that underpins all AI and ML capabilities across the company. This platform serves as the essential backbone for model development, deployment, and ongoing monitoring, ensuring that machine learning models can be reliably used at scale. This role is crucial because it involves creating the infrastructure—including feature engineering, training, deployment, and serving systems—that allows data scientists to ship models efficiently and safely. The goal is to provide a self-service, reproducible, and production-safe environment for all ML work.
This Senior Software Engineer will be responsible for architecting and constructing this unified ML platform from the ground up. The individual will own the end-to-end delivery of scalable services, focusing on creating tooling and guardrails that enable data scientists to operate independently while maintaining necessary validation and rollback mechanisms. Success in this role requires a strong blend of software engineering discipline and an understanding of machine learning workflows.
This position is ideal for an engineer who thrives on platform ownership, enjoys solving complex distributed systems challenges, and possesses the autonomy to define technical direction. Candidates who are comfortable bridging the gap between data science needs and robust, production-grade infrastructure will find this role very suitable.
What you will do
- Architect and build DailyPay's unified ML platform, encompassing feature engineering, model training, deployment, and serving infrastructure.
- Design and develop scalable, reliable services and pipelines for feature generation, model training, and inference.
- Implement tooling and guardrails that allow data scientists to independently define, test, and ship features and models.
- Manage and optimize AWS infrastructure specifically for machine learning workloads, focusing on cost, security, and availability.
- Develop and maintain robust CI/CD pipelines for the continuous integration and deployment of ML models.
- Design monitoring and alerting systems for ML infrastructure and models to proactively identify and resolve production issues.
- Lead technical design discussions and contribute to architectural decisions for the ML platform.
What you need
- Possesses at least five years of professional software engineering experience building and operating production services.
- Demonstrates a strong background in distributed systems, service-oriented architecture, and API design principles.
- Has experience across the full software lifecycle, including design, testing, deployment, and on-call operations.
- Is proficient in Python, with a proven track record of writing production-quality, tested, and maintainable code.
- Familiarity with ML frameworks such as scikit-learn, XGBoost, and PyTorch, and understanding their application in AI workflows.
- Experience with event streaming platforms like Apache Kafka or equivalent systems is required.
- Must have experience with infrastructure-as-code tools such as Terraform or CloudFormation.
- Experience with containerization and orchestration technologies like Docker and Kubernetes or ECS is necessary.
- Strong proficiency in cloud platforms, with AWS experience (SageMaker, Lambda, S3, EC2, IAM, ECS) being preferred.
Nice to have
- Experience with GCP Vertex AI
- Experience with Datadog, Prometheus, or Grafana
- Experience with dbt, Glue, or Snowflake for data pipeline tooling
Benefits
- $170K – $190K salary
Skills and tech stack
Questions candidates ask
What is the remote policy for this role?
This is a US-Remote position. The job is based in the US.
What is the expected salary range?
The salary for this role is listed between $170K and $190K.
What is the primary technology stack used for this platform?
The role involves working primarily in Python, Scala, Go, and managing infrastructure as code with Terraform.
What kind of team structure is involved?
The engineer will work closely with data scientists, engineers, and product stakeholders.