Senior Machine Learning Engineer

DailyPay; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

DailyPay, a worktech company in New York City, is seeking a Senior Machine Learning Engineer to mature and scale our ML infrastructure. You will drive reliability, performance, and scalability of models in production and own end-to-end MLOps in a fast-paced environment.

Collaborating with data scientists, engineers, and product teams, you will lead platform design, implement scalable pipelines on AWS, and mentor junior engineers while upholding security and compliance standards.

Qualifications

• 5+ years in ML engineering, MLOps, or data engineering.
• Strong cloud experience: AWS (SageMaker, Lambda, S3, EC2, IAM, ECS) or GCP/Azure.
• Proficiency in Python and ML frameworks (scikit-learn, TensorFlow, PyTorch).
• Solid CI/CD experience (GitHub Actions) and deployment pipelines.
• Experience with infrastructure-as-code (Terraform or CloudFormation).
• Knowledge of event streaming platforms (Kafka).
• Experience with monitoring/observability tools (Datadog, Prometheus, Grafana).
• Strong SQL and data pipeline tools (dbt, Glue, Snowflake).
• Excellent communication across data science, engineering, and product teams.

Responsibilities

• Platform Ownership: architect and build DailyPay's unified ML platform for development, deployment, and monitoring.
• MLOps Architecture & Delivery: scalable pipelines for training, deployment, monitoring, and retraining.
• Cloud Infrastructure: manage and optimize AWS infra for ML workloads.
• CI/CD Pipeline Development: robust pipelines for ML models and infra.
• Monitoring & Observability: design monitoring and alerting for ML infra using Datadog.
• Technical Leadership: lead design discussions and architectural decisions; remove blockers.
• Mentorship: mentor junior engineers and share domain knowledge.
• Security & Compliance: ensure pipelines handle sensitive data securely and in line with policy.

Skills

ML / MLOps

Cloud platforms

Python

CI/CD

IaC

Kafka

Monitoring

SQL

Communication

Tools

Terraform

CloudFormation

Datadog

Prometheus

Grafana

Docker

Kubernetes

SageMaker

Lambda

S3

EC2

Job description

About Us:
DailyPay is transforming the way people get paid. As a worktech company and the industry’s leading on demand pay solution, DailyPay uses an award-winning technology platform to help America’s top employers build stronger relationships with their employees. This voluntary employee benefit enables workers everywhere to feel more motivated to work harder and stay longer on the job while supporting their financial well‑being outside of the workplace.
DailyPay is headquartered in New York City, with operations throughout the United States as well as in Belfast. For more information, visit DailyPay's Press Center.

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. 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.

How You Will Make an Impact:

• 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.

What You Bring to the Team:

• 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

High‑performing cultures aren't built in silos, they thrive on partnership. At DailyPay, we Commit Together to an inclusive, professional environment where multifaceted perspectives are our greatest competitive advantage. We recognize that our team members don’t live “single-issue lives,” and we lean into the wide-ranging backgrounds and life stages that sharpen our collective decision‑making.

In our high‑trust environment, we empower you to Challenge Norms. We’ve created a space where it is safe to ask difficult questions, disrupt the status quo, and share bold perspectives without fear of professional fallout. We believe that by checking our own assumptions and staying curious about the experiences of others, we arrive at better, more innovative results.

We provide the space for you to do your best work through peer advocacy and transparent career development. If you are looking for a culture that values intellectual honesty, celebrates the unique lived experiences of its people, and thrives on collective success, you’ll find it here.

If you require reasonable accommodation for any aspect of the recruitment process, please send a request to [e-mail]. All requests for accommodation will be addressed as confidentially as practicable.

DailyPay is an equal opportunity employer. All qualified applicants will receive consideration without regard to race, color, religion or creed, alienage or citizenship status, political affiliation, marital or partnership status, age, national origin, ancestry, physical or mental disability, medical condition, veteran status, gender, gender identity, pregnancy, childbirth (or related medical conditions), sex, sexual orientation, sexual and other reproductive health decisions, genetic disorder, genetic predisposition, carrier status, military status, familial status, or domestic violence victim status and any other basis protected under federal, state, or local laws.