Senior Machine Learning Engineer
DailyPay2 weeks ago
Location
NYC Headquarters
Type
Full Time
Salary
USD 190,000 – 250,000
Level
Senior
Role
ML Engineer
Posted
Jul 6, 2026
Full TimeSenior
The role
Summary
Join DailyPay's AI & ML team as a Senior Machine Learning Engineer to architect and scale our unified ML platform supporting on-demand pay solutions for America's leading employers. This role requires 5+ years of MLOps and cloud infrastructure expertise, with hands-on proficiency in AWS, Python, CI/CD pipelines, and data engineering tools to deliver production-grade ML systems that impact millions of workers' financial flexibility.
What you'll do
ML Platform Architecture and Development: Design, architect, and build DailyPay's unified ML platform serving as the backbone for all AI and ML capabilities company-wide. This includes establishing standardized frameworks for model development, deployment, and monitoring that enable data scientists and engineers to collaborate efficiently at scale.
MLOps Pipeline Design and Implementation: Engineer and maintain scalable end-to-end ML pipelines covering model training, deployment, monitoring, and retraining. Take ownership of MLOps solution delivery with minimal oversight, ensuring pipelines are robust, maintainable, and follow industry best practices for production ML systems.
AWS Infrastructure Optimization: Manage, optimize, and scale AWS infrastructure for machine learning workloads including SageMaker, Lambda, S3, EC2, and ECS resources. Balance cost-effectiveness, security compliance, and high availability while supporting multiple ML teams and production models serving critical business functions.
CI/CD Pipeline Development: Build and maintain sophisticated CI/CD pipelines for continuous integration and deployment of ML models and supporting infrastructure. Implement automated testing, validation, and deployment strategies using GitHub Actions and infrastructure-as-code tools to enable rapid, safe model iterations.
Monitoring, Observability, and Incident Response: Design comprehensive monitoring and alerting systems for ML infrastructure and production models using Datadog and other observability tools. Proactively identify performance degradation, data drift, and infrastructure issues before they impact end users. Lead incident response and post-mortems to drive continuous improvement.
Technical Leadership and Architectural Decision-Making: Lead design discussions and contribute to foundational architectural decisions affecting ML systems across the organization. Establish team norms and best practices for how ML systems are built, tested, deployed, and maintained. Help identify technical blockers and drive solutions that unblock team progress.
Mentorship and Knowledge Transfer: Mentor junior engineers and data engineers on MLOps principles, cloud infrastructure, and production ML best practices. Share domain expertise through code reviews, design discussions, and informal knowledge-sharing sessions to build deep technical capabilities across the AI & ML team.
Security and Compliance in ML Systems: Approach all engineering work with a security-first mindset, actively identifying vulnerabilities in code and infrastructure during peer reviews. Ensure ML pipelines handle sensitive financial data in accordance with company security policies and regulatory compliance requirements. Implement IAM best practices and data governance throughout the ML platform.
What we look for
Technical
Python ProficiencyAdvanced Python programming skills with hands-on experience building production ML systems. Proficiency in writing efficient, maintainable code with strong testing practices and debugging capabilities.
AWS Cloud Platform ExpertiseDeep proficiency with AWS services including SageMaker for model training and deployment, Lambda for serverless compute, S3 for data storage, EC2 for compute instances, IAM for identity and access management, and ECS for container orchestration. Understanding of AWS networking, storage optimization, and cost management.
ML Framework ExperienceHands-on experience with scikit-learn, TensorFlow, and/or PyTorch for building and training machine learning models. Understanding of model architecture, hyperparameter tuning, and optimization techniques.
CI/CD and Deployment PipelinesSolid experience designing and operating continuous integration and deployment pipelines using GitHub Actions or equivalent tools. Proficiency in version control, automated testing, and deployment automation strategies.
Infrastructure-as-CodePractical experience with Terraform or CloudFormation for defining and managing cloud infrastructure programmatically. Ability to version control infrastructure definitions and implement infrastructure changes through code review processes.
Event Streaming PlatformsKnowledge of distributed event streaming architecture and hands-on experience with Apache Kafka or equivalent platforms for building real-time data pipelines and handling high-volume data streams.
Monitoring and ObservabilityExperience with Datadog, Prometheus, Grafana, or similar monitoring and observability tools. Ability to design alerting strategies, create meaningful dashboards, and troubleshoot production issues using observability data.
SQL and Data Pipeline ToolsStrong SQL skills for data analysis and optimization. Experience with data pipeline tools such as dbt for data transformation, AWS Glue for ETL workflows, and Snowflake or similar data warehousing platforms.
Education
Computer Science or Related FieldBachelor's degree in Computer Science, Software Engineering, Mathematics, Physics, or equivalent field. Advanced degree (Master's or PhD) in related discipline is a plus but not required if strong professional experience is demonstrated.
Experience
5+ Years ML Engineering / MLOps ExperienceMinimum 5 years of professional experience in machine learning engineering, MLOps, or data engineering roles. Track record of successfully shipping production ML systems and scaling ML infrastructure to support multiple teams and models.
Production ML Systems at ScaleProven experience architecting and maintaining machine learning systems in production environments handling real-world data complexity, model performance requirements, and reliability standards.
Cross-Functional CollaborationDemonstrated ability working effectively across data science, software engineering, and product teams. Experience communicating technical concepts to non-technical stakeholders and bridging the gap between research and production.
Infrastructure and DevOps MindsetBackground in infrastructure engineering, DevOps, or similar disciplines with understanding of deployment strategies, observability, reliability engineering, and operational best practices.
Skills
Required skills
PythonCore programming language for ML systems, data processing, and infrastructure automation
AWS (SageMaker, Lambda, S3, EC2, IAM, ECS)Cloud platform expertise for deploying and managing ML workloads and infrastructure
Machine Learning Frameworks (TensorFlow, PyTorch, scikit-learn)Practical experience building and training production ML models
CI/CD and GitHub ActionsDesigning and maintaining deployment pipelines for models and infrastructure
Terraform or CloudFormationInfrastructure-as-code tools for managing cloud resources programmatically
Apache KafkaEvent streaming platform for real-time data pipeline architecture
Datadog or Similar Monitoring ToolsProduction observability and monitoring for ML systems and infrastructure
SQL and Data WarehousingData querying, analysis, and experience with tools like Snowflake, dbt, or AWS Glue
Nice to have
Docker and KubernetesContainerization and orchestration experience for deploying ML models and services at scale
Microservices ArchitectureUnderstanding of distributed systems design patterns and RESTful API development
Fintech or Regulated Industry ExperienceBackground in financial services, payments, or other heavily regulated industries brings valuable perspective on compliance and security requirements
Open-Source ML/MLOps ContributionsActive contributions to open-source projects in machine learning or MLOps ecosystems demonstrates community engagement and technical depth
GCP (Vertex AI, Cloud Functions) or Azure (Azure ML, AKS)Alternative cloud platform experience demonstrates portability of cloud architecture skills
Compensation & benefits
Salary
USD 190,000 – 250,000 (annual)
Stock options
Available
Benefits
Equity / Stock Options
Participate in DailyPay's long-term success through competitive equity packages aligning your interests with company growth
Comprehensive Health Insurance
Medical, dental, and vision coverage with DailyPay contributing significantly to premiums
401(k) Retirement Plan
401(k) retirement savings plan with employer matching to support your long-term financial planning
Flexible PTO and Paid Time Off
Flexible paid time off policy combined with competitive vacation days to support work-life balance
Professional Development Budget
Annual learning and development budget to invest in certifications, conferences, and skill-building opportunities
On-Demand Pay Access
Access to DailyPay's on-demand pay benefit as an employee, providing financial flexibility and wage access
Remote Work Flexibility
Flexible work arrangements supporting both in-office collaboration and remote work options
Mental Health and Wellness Programs
Mental health resources, wellness programs, and employee assistance programs supporting holistic well-being
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DailyPay
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DailyPay is a leading on-demand pay platform, enabling employees to access their earned wages before payday, providing greater financial flexibility and control.
New York, NY, USAFounded 2014dailypay.com
Tech Stack
Languages
PythonSQL
Frameworks
TensorFlowPyTorchscikit-learn
Databases
SnowflakeAWS S3
Tools
AWS SageMakerAWS LambdaAWS EC2AWS ECSGitHub ActionsTerraformApache KafkaDatadogdbtAWS Glue
Other
DockerKubernetesRESTful API DesignIAM (Identity and Access Management)