Note: The job is a remote job and is open to candidates in USA. DailyPay is a leader in on-demand pay, helping employers modernize how people access their pay and providing financial wellness tools. The Senior Software Engineer will build and own the ML platform infrastructure supporting feature engineering, model training and deployment, serving, monitoring, and reliable production operations. The role involves collaborating with data scientists, engineers, and product stakeholders while providing technical leadership and mentorship.

Responsibilities

• 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
• Design and build scalable, reliable services and pipelines covering feature generation, model training, deployment, and inference. Own end-to-end delivery with minimal oversight
• 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
• Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability. Experience in AWS SageMaker or GCP Vertex is ideal
• 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
• 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
• Mentor junior engineers. Share domain knowledge and help build genuine technical depth on the team
• 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

Skills

• 5+ years of professional software engineering experience building and operating production services
• Strong background in distributed systems, service-oriented architecture, and API design
• Experience across the full software lifecycle: design, testing, deployment, and on-call operations
• Proficiency in Python, with a track record of writing production-quality, tested, maintainable code
• Familiarity with ML frameworks (scikit-learn, XGBoost, PyTorch) and how they are used by AI and Data Scientists
• Experience with event streaming platforms (Apache Kafka or equivalent)
• Experience with infrastructure-as-code (Terraform or CloudFormation), including module design and environment separation
• Solid CI/CD experience: GitHub Actions or equivalent; designing and operating deployment pipelines
• Experience with containerization and orchestration (Docker, and Kubernetes or ECS)
• Experience building or operating ML infrastructure: training pipelines, model serving, feature stores, or model registries
• Or equivalent GCP (Vertex AI, Cloud Functions, GCS, Compute Engine, Cloud Run) or Azure (Azure ML, Functions, Blob Storage, VMs, AKS) experience
• 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
• Strong cloud platform proficiency: AWS preferred (SageMaker, Lambda, S3, EC2, IAM, ECS)
• Experience with experimentation infrastructure and A/B testing systems
• Experience in fintech or other regulated industries
• Contributions to open-source infrastructure, platform, or MLOps projects

Benefits

• Remote work
• Peer advocacy
• Transparent career development

Company Overview

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