Senior Machine Learning Engineer

DailyPay is a provider of on-demand pay and financial wellness solutions that helps employers modernize how people get paid. The Senior Machine Learning Engineer will mature and scale machine learning infrastructure, design end-to-end MLOps solutions, manage cloud infrastructure, and ensure the reliability, performance, and scalability of production ML systems.

Responsibilities:

• Platform Ownership: Help architect and build DailyPay's unified ML platform - a unified system for model development, deployment, and monitoring that serves as the backbone for every AI and ML capability at the company
• MLOps Architecture & Delivery: Design and implement scalable ML pipelines covering model training, deployment, monitoring, and retraining. Own the delivery of end-to-end MLOps solutions with minimal oversight
• Cloud Infrastructure: Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability
• CI/CD Pipeline Development: Build and maintain robust CI/CD pipelines for continuous integration and deployment of ML models and related infrastructure
• Monitoring & Observability: Design monitoring and alerting systems for ML infrastructure and models using tools like Datadog. Proactively identify and resolve issues before they impact production
• Technical Leadership: Lead design discussions, contribute to architectural decisions, and establish team norms for how ML systems are built, tested, and maintained. Help identify and remove blockers
• Mentorship: Mentor junior engineers. Share domain knowledge and help build genuine technical depth on the team
• Security & Compliance: Approach all engineering work with a security lens. Actively look for vulnerabilities in code and during peer reviews. Ensure ML pipelines handle sensitive data in accordance with company policy

Requirements:

• • 5+ years of experience in machine learning engineering, MLOps, or data engineering
• • Strong cloud platform proficiency: AWS preferred (SageMaker, Lambda, S3, EC2, IAM, ECS), or equivalent GCP (Vertex AI, Cloud Functions, GCS, Compute Engine, Cloud Run) or Azure (Azure ML, Functions, Blob Storage, VMs, AKS) experience
• • Proficiency in Python and experience with ML frameworks (scikit-learn, TensorFlow, PyTorch)
• • Solid CI/CD experience: GitHub Actions or equivalent; designing and operating deployment pipelines
• • Experience with infrastructure-as-code (Terraform or CloudFormation)
• • Knowledge of event streaming platforms (Apache Kafka or equivalent)
• • Experience with monitoring and observability tooling (Datadog, Prometheus, or Grafana)
• • Strong SQL skills and experience with data pipeline tooling (dbt, Glue, Snowflake)
• • Excellent communication skills; comfortable working across data science, engineering, and product teams
• • Experience with containerization and orchestration (Docker, Kubernetes)
• • Familiarity with microservices architecture and RESTful API design
• • Experience in fintech or regulated industries
• • Contributions to open-source ML or MLOps projects