Senior Software Engineer, ML Platform DailyPay · Remote · US · ML Platform & Ops $170,000–$190,000 10d ago

Aimlroles; Minneapolis (MN); Ibrido; USD 150.000 - 210.000; Tempo pieno

Descrizione del lavoro

DailyPay is seeking a Senior Software Engineer to build its ML platform from the ground up, owning the infrastructure for feature generation, training, deployment and monitoring. You will work closely with data scientists, engineers and product stakeholders to deliver high-quality ML solutions with substantial autonomy.

You will design scalable services, self-service tooling, and robust CI/CD for ML workloads on AWS or via GCP/Azure equivalents, while mentoring others and maintaining strong

Competenze

• 5+ years of professional software engineering experience building and operating production services.
• Strong background in distributed systems, service-oriented architecture, and API design.
• Experience across the full software lifecycle: design, testing, deployment, and on-call operations.
• Proficiency in Python, with production-quality code and testing practices.
• Familiarity with ML frameworks (scikit-learn, XGBoost, PyTorch) and ML workflows.
• Experience with event streaming (Apache Kafka or equivalent).
• Infrastructure-as-code (Terraform or CloudFormation) and environment separation.
• CI/CD experience with GitHub Actions or equivalent; deployment pipelines.
• Containerization and orchestration (Docker, Kubernetes or ECS).
• Experience building or operating ML infrastructure (training pipelines, serving, feature stores).
• Cloud proficiency (AWS preferred; SageMaker, Lambda, S3, EC2, IAM, ECS) or GCP/Azure equivalents.
• Monitoring/observability tooling (Datadog, Prometheus, Grafana).
• Strong SQL skills; data pipeline tooling (dbt, Glue, Snowflake).
• Excellent communication across data science, engineering, and product teams.

Mansioni

• Platform Ownership: architect and build DailyPay's unified ML platform from the ground up.
• Systems Design & Delivery: design scalable services and end-to-end pipelines for feature generation, training, deployment, and inference.
• Self-Service Infrastructure: build tooling and guardrails for independent feature/model definition and validation.
• Cloud Infrastructure: manage and optimize AWS for ML workloads with cost, security, and availability in mind.
• CI/CD Pipeline Development: create robust pipelines for ML models and related infra.
• Monitoring & Observability: design monitoring and alerting to catch issues before production impact.
• Technical Leadership: lead design discussions and establish team norms; remove blockers.
• Mentorship: mentor junior engineers and share domain knowledge.
• Security & Compliance: ensure ML pipelines handle sensitive data securely and per policy.

Conoscenze

Python

Distributed systems

API design

ML frameworks

Kafka

Terraform

CI/CD

Docker

Kubernetes

AWS SageMaker

GCP Vertex AI

Datadog

SQL

Strumenti

Terraform

Docker

Kubernetes

GitHub Actions

Datadog

Prometheus

Grafana

Snowflake

dbt

Glue

Descrizione del lavoro

About Us:
DailyPay is the leader in On-Demand Pay, helping employers modernize how people get their pay. DailyPay serves more than 1,900 employers and over 6 million employees, including many of the world's most recognized brands. By providing real-time access to earned pay and financial wellness tools, DailyPay helps employees manage their finances and helps employers attract and retain talent. DailyPay is helping define the future of pay, where money moves at the speed of work. Learn more at DailyPay's Press Center.

The Role

We are seeking a Senior Software Engineer to build DailyPay's ML platform from the ground up. You will design and build the infrastructure that every machine learning model at DailyPay runs on: feature engineering platform, model training and deployment, serving infrastructure, and the monitoring that keeps it all reliable in production.

This is a software engineering role, primarily working in Python, Scala, Go, and managing infra as code with Terraform. You will build the platform that data scientists use to ship models, not build the models themselves. You own the infrastructure that makes their work reproducible, testable, observable, and production-safe at scale.

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, design, and build DailyPay's unified ML platform from the ground up. Our ML Platform is a new unified system for model development, deployment, and monitoring that serves as the backbone for every AI and ML capability at the company.

• Systems Design & Delivery: Design and build scalable, reliable services and pipelines covering feature generation, model training, deployment, and inference. Own end-to-end delivery with minimal oversight.

• Self-Service Infrastructure: Build the tooling and guardrails that let data scientists define, test, and ship features and models independently, without needing an engineer in the loop and without bypassing validation, lineage, or rollback safeguards.

• Cloud Infrastructure: Manage and optimize AWS infrastructure for machine learning workloads, balancing cost-effectiveness, security, and availability. Experience in AWS SageMaker or GCP Vertex is ideal.

• 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 professional software engineering experience building and operating production services

• Strong background in distributed systems, service-oriented architecture, and API design

• Experience across the full software lifecycle: design, testing, deployment, and on-call operations

• Proficiency in Python, with a track record of writing production-quality, tested, maintainable code

• Familiarity with ML frameworks (scikit-learn, XGBoost, PyTorch) and how they are used by AI and Data Scientists.

• Experience with event streaming platforms (Apache Kafka or equivalent).

• Experience with infrastructure-as-code (Terraform or CloudFormation), including module design and environment separation

• Solid CI/CD experience: GitHub Actions or equivalent; designing and operating deployment pipelines

• Experience with containerization and orchestration (Docker, and Kubernetes or ECS)

• Experience building or operating ML infrastructure: training pipelines, model serving, feature stores, or model registries

• 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

• 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 experimentation infrastructure and A/B testing systems

• Experience in fintech or other regulated industries

• Contributions to open-source infrastructure, platform, 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.