Senior Machine Learning Engineer

About the role

We are seeking a Senior Machine Learning Engineer to join our AI & ML team in New York City. You will play a key role in maturing and scaling our machine learning infrastructure, ensuring the reliability, performance, and scalability of ML models in production.

Responsibilities

• 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.
• 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.
• Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability.
• 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.

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)
• 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

Qualifications

• Nice to haves: 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