Software Engineer III - Machine Learning Platform
On-site · New York City, New York, United States
Job Summary
Software Engineer III (Machine Learning Platform Engineer) at JPMorgan Chase designs, builds, and maintains scalable ML platforms and infrastructure to support end-to-end ML workflows. Responsibilities include developing tools for model training, deployment, monitoring, and lifecycle management; integrating data engineering, feature management, and model serving into unified platform solutions; delivering secure production-grade code for platform services and automation pipelines; collaborating with data scientists and product teams to accelerate ML development and operations; ensuring platform reliability and performance; producing architecture artifacts aligned with enterprise standards; automating infrastructure provisioning and CI/CD pipelines for ML services; and contributing to the ML platform engineering community of practice and exploring emerging technologies.
Required Qualifications
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure
- Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)
- Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure
- Strong understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring
- Experience developing APIs and platform services for ML workflows
- Solid knowledge of the software development life cycle and agile methodologies
- Ability to collaborate with cross-functional teams to deliver platform solutions aligned with business objectives
- Familiarity with Databricks for scalable data engineering and ML platform integration
- Experience working with Snowflake for cloud-based data warehousing and analytics
- Exposure to Snorkel AI for programmatic data labeling and training data management
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)
- Familiarity with feature stores, model registries, and ML metadata management
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)
- Experience with RESTful APIs and microservices architectures
Desired Qualifications
- Formal training or certification on software engineering concepts and 3+ years applied experience
- Hands-on experience building, deploying, and maintaining machine learning platforms or infrastructure
- Proficiency in Python and one or more ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
- Experience with data processing frameworks and tools (e.g., Spark, Pandas, SQL)
- Practical experience with cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure
- Strong understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring
- Experience developing APIs and platform services for ML workflows
- Solid knowledge of the software development life cycle and agile methodologies
- Ability to collaborate with cross-functional teams to deliver platform solutions aligned with business objectives
- Familiarity with Databricks for scalable data engineering and ML platform integration
- Experience working with Snowflake for cloud-based data warehousing and analytics
- Exposure to Snorkel AI for programmatic data labeling and training data management
- Experience with containerization and orchestration tools (e.g., Docker, Kubernetes, Airflow)
- Familiarity with feature stores, model registries, and ML metadata management
- Experience with infrastructure-as-code tools (e.g., Terraform, CloudFormation)
- Experience with RESTful APIs and microservices architectures
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