DailyPay - Senior Software Engineer, ML Platform
Requirements
• 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 • 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 • 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 experimentation infrastructure and A/B testing systems • Experience in fintech or other regulated industries • Contributions to open-source infrastructure, platform, or MLOps projects • High-performing cultures aren't built in silos, they thrive on partnership. At DailyPay, we Commit Together to an inclusive, professional environment where multifaceted perspectives are our greatest competitive advantage. We recognize that our team members don’t live “single-issue lives,” and we lean into the wide-ranging backgrounds and life stages that sharpen our collective decision-making. • Commit Together • In our high-trust environment, we empower you to Challenge Norms. We’ve created a space where it is safe to ask difficult questions, disrupt the status quo, and share bold perspectives without fear of professional fallout. We believe that by checking our own assumptions and staying curious about the experiences of others, we arrive at better, more innovative results. • Challenge Norms • We provide the space for you to do your best work through peer advocacy and transparent career development. If you are looking for a culture that values intellectual honesty, celebrates the unique lived experiences of its people, and thrives on collective success, you’ll find it here. • If you require reasonable accommodation for any aspect of the recruitment process, please send a request to [email protected]. All requests for accommodation will be addressed as confidentially as practicable.
Responsibilities
• 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. • Platform Ownership: • 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. • Systems Design & Delivery: • 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. • Self-Service Infrastructure: • 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. • Cloud Infrastructure: • CI/CD Pipeline Development: Build and maintain robust CI/CD pipelines for continuous integration and deployment of ML models and related infrastructure. • CI/CD Pipeline Development: • 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. • Monitoring & Observability: • 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. • Technical Leadership: • Mentorship: Mentor junior engineers. Share domain knowledge and help build genuine technical depth on the team. • Mentorship: • 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. • Security & Compliance:
Benefits
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