Bulge Bracket Investment Banks

Software Engineer II - Machine Learning Platform

at J.P. Morgan

JuniorNo visa sponsorshipPython

Posted 5 days ago

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Compensation: Not specified USD
City: Palo Alto
Country: United States

Join JPMorgan Chase as a Software Engineer II on the ML Platform team to design, build, and maintain scalable machine learning infrastructure that supports end-to-end ML workflows. You will develop tools for model training, deployment, monitoring, and lifecycle management, and write secure production code for platform services and APIs. You will collaborate with data scientists, ML engineers, and product teams to deliver platform features that accelerate ML development and operations, while helping ensure reliability and performance. You will grow your ML platform engineering skills by working with senior engineers and contributing to architecture and automation of infrastructure and CI/CD pipelines.

Location: Palo Alto, CA, United States

Advance your career by contributing to the development of robust machine learning platforms at JPMorgan Chase, gaining valuable skills and experience as you help drive innovative solutions for data scientists and ML engineers.

As a Software Engineer II at JPMorgan Chase in the Consumer and Community Banking Technology team, you will be an integral part of an agile engineering team focused on enhancing, designing, and delivering secure, stable, and scalable technology products. Leveraging your technical skills and eagerness to learn, you will support the development and maintenance of infrastructure and tools that enable efficient model development, deployment, and monitoring. Working alongside senior engineers, you will deepen your expertise in ML platform engineering, learn best practices, and help shape the future of machine learning at JPMorgan Chase.

Job Responsibilities

Assist in designing, building, and maintaining scalable machine learning platforms and infrastructure to support end-to-end ML workflows.

Develop and enhance tools for model training, deployment, monitoring, and lifecycle management under the guidance of senior engineers.

Support the integration of data engineering, feature management, and model serving capabilities into unified ML platform solutions.

Write secure, high-quality production code for platform services, APIs, and automation pipelines.

Collaborate with data scientists, ML engineers, and product teams to understand requirements and deliver platform features that accelerate ML development and operations.

Participate in ensuring platform reliability, scalability, and performance through monitoring, troubleshooting, and continuous improvement.

Contribute to architecture and design documentation for platform components, ensuring alignment with enterprise standards.

Help automate infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.

Engage with the ML platform engineering community and participate in events to learn about new and emerging technologies.

Required Qualifications, Capabilities, and Skills

2+ years of applied experience or formal training/certification in software engineering concepts.

Hands-on practical experience in system design, application development, testing, and operational stability.

Experience in developing, debugging, and maintaining code in a large corporate environment with one or more modern programming languages and database querying languages.

Proficiency in Python and familiarity with at least one ML framework (e.g., TensorFlow, PyTorch, Scikit-learn).

Experience with data processing tools (e.g., Spark, Pandas, SQL).

Exposure to cloud-based ML platforms (e.g., AWS SageMaker, GCP AI Platform, Azure ML) or on-prem ML infrastructure.

Understanding of MLOps practices, including CI/CD for ML, model versioning, and monitoring.

Experience developing APIs or platform services for ML workflows.

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.

Preferred Qualifications, Capabilities, and Skills

Familiarity with Databricks for scalable data engineering and ML platform integration.

Experience 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.

This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position is contingent on JPMorgan Chase’s review of criminal conviction history, including pretrial diversions or program entries.

Serve as an emerging member of an agile team to design and deliver market-leading technology products in a secure and scalable way

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