Senior Machine Learning Engineer

DailyPay

Senior Machine Learning Engineer focused on enhancing ML infrastructure at DailyPay. Requires deep MLOps experience and collaboration with data scientists and engineers.

Posted 7/6/2026full-timeNew York City • New York • 🇺🇸 United StatesSenior💰 $190,000 - $250,000 per yearWebsite

Tech Stack

Tools & technologies

ApacheAWSAzureCloudEC2Google Cloud PlatformGrafanaKafkaPrometheusPythonPyTorchScikit-LearnSQLTensorflowTerraform

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

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Hard Skills & Tools

Machine Learning EngineeringMLOpsPythonCI/CDSQLInfrastructure-as-CodeEvent StreamingMonitoring and ObservabilityData Pipeline ToolingML Frameworks

Soft Skills

Excellent CommunicationMentoring