Note: The job is a remote job and is open to candidates in USA. DailyPay is an on-demand pay and financial wellness company. The Senior Machine Learning Engineer will mature and scale machine learning infrastructure, build reliable and scalable production ML systems, and collaborate with data scientists, engineers, and product stakeholders to deliver ML solutions. The role also provides technical leadership, mentorship, and ownership of MLOps, cloud infrastructure, monitoring, security, and compliance.
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
Skills
- • 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
Benefits
- Remote work
- An inclusive, professional environment
- Peer advocacy and transparent career development
Company Overview
Company H1B Sponsorship