Senior Machine Learning Engineer
About The Position
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.
Requirements
- 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
Responsibilities
- 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.
Benefits
- 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.
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
