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# Senior Machine Learning Engineer

- **Company:** DailyPay Inc
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $190,000.0 - $250,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, Database Queries, Github, Monitoring of Systems, Identity and Access Management, Python (Programming Language), Machine Learning, Open Source Technology, Tensorflow, Prometheus, Azure Machine Learning, Data Streaming, Datadog, Cloud Platform System, Pytorch, Delivery Pipeline, Snowflake, Grafana, Cloudformation, Containerization, Scikit Learn, Kubernetes, Apache Kafka, Machine Learning Operations, Functional Programming, Restful APIs, Terraform, Data Pipelines, Docker, Microservices
- **Published:** July 8, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=fdb05c2c61999715

## About the Role

* 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

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

## Description

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. This role requires deep hands-on experience with MLOps principles, cloud infrastructure, and a track record of delivering robust ML systems in a fast-moving environment.

You will work closely with data scientists, engineers, and product stakeholders to deliver high-quality ML solutions that directly impact DailyPay's core products. You are expected to operate with significant autonomy: defining work, identifying dependencies, and raising the bar for the team around you., * 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.


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