Senior Machine Learning Engineer

dailypay; United States; Remote; USD 140,000 - 200,000; Full time

Job summary

DailyPay is seeking a senior ML Engineer to mature and scale the ML platform powering the on-demand pay product. You will own MLOps architecture, cloud infrastructure, and the reliability of production ML models, partnering with data science, software, and product teams in a fast-moving environment.

You will architect a unified ML platform, design scalable training and deployment pipelines, manage cost-effective cloud infra, and lead CI/CD for ML workloads while mentoring engineers and enforcing

Qualifications

• 5+ years of experience in ML engineering, MLOps, or data engineering.
• Proficiency with AWS services (SageMaker, Lambda, S3, EC2, IAM, ECS) or equivalent GCP/Azure.
• Strong Python skills and ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Hands-on CI/CD experience using GitHub Actions or equivalents.
• Familiarity with infrastructure-as-code using Terraform or CloudFormation.
• Experience with event streaming platforms such as Apache Kafka.
• Exposure to monitoring/observability tools like Datadog, Prometheus, or Grafana.
• Strong SQL and data pipeline tooling such as dbt, Glue, or Snowflake.

Responsibilities

• Help architect and evolve a unified ML platform spanning model development, deployment, and monitoring.
• Design and implement scalable pipelines for training, deployment, monitoring, and retraining of ML models.
• Manage and optimize cloud infrastructure to balance cost, security, and availability for ML workloads.
• Build and maintain CI/CD pipelines for ML models and supporting infrastructure.
• Implement monitoring, alerting, and observability for ML systems and models.
• Lead design discussions, contribute to architectural decisions, and set team norms for building and maintaining ML systems.
• Mentor junior engineers and help grow technical depth across the team.
• Apply a security mindset across code, reviews, and data handling practices.

Skills

ML engineering

MLOps

Python

AWS SageMaker

CI/CD

Terraform

Kafka

Datadog

Prometheus

SQL

dbt

Snowflake

GitHub Actions

Tools

Docker

Kubernetes

Job description

Role overview

A senior engineering role focused on maturing and scaling the machine learning platform that powers an on-demand pay product. The position emphasizes MLOps architecture, cloud infrastructure, and the reliability and performance of production ML models. The engineer partners across data science, software engineering, and product teams and operates with significant autonomy in a fast-moving environment.

Responsibilities

• Help architect and evolve a unified ML platform spanning model development, deployment, and monitoring
• Design and implement scalable pipelines for training, deployment, monitoring, and retraining of ML models
• Manage and optimize cloud infrastructure to balance cost, security, and availability for ML workloads
• Build and maintain CI/CD pipelines for ML models and supporting infrastructure
• Implement monitoring, alerting, and observability for ML systems and models
• Lead design discussions, contribute to architectural decisions, and set team norms for building and maintaining ML systems
• Mentor junior engineers and help grow technical depth across the team
• Apply a security mindset across code, reviews, and data handling practices

Requirements

• Five or more years of experience in machine learning engineering, MLOps, or data engineering
• Proficiency with AWS services such as SageMaker, Lambda, S3, EC2, IAM, and ECS, or equivalent experience with GCP or Azure
• Strong Python skills and experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch
• Hands-on CI/CD experience using tools like GitHub Actions or equivalents
• Familiarity with infrastructure-as-code using Terraform or CloudFormation
• Experience with event streaming platforms such as Apache Kafka
• Exposure to monitoring and observability tools like Datadog, Prometheus, or Grafana
• Strong SQL skills and experience with data pipeline tooling such as dbt, Glue, or Snowflake

Nice to have

• Experience with containerization and orchestration using Docker and Kubernetes
• Familiarity with microservices architecture and RESTful API design
• Background in fintech or other regulated industries
• Contributions to open-source ML or MLOps projects

Benefits and work setup

• Position is based in New York City within an AI and ML team
• Description highlights an inclusive, high-trust culture with peer advocacy and transparent career development