Bulge Bracket Investment Banks

Manager of Software Engineering - Machine Learning Platform

at J.P. Morgan

Mid LevelNo visa sponsorshipData Science/AI/ML

Posted 5 days ago

No clicks

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

Lead and manage multiple teams to design, develop, and maintain scalable ML platforms and infrastructure. Set strategic direction for ML platform initiatives and ensure alignment with business goals. Oversee tool delivery for model training, deployment, monitoring, and lifecycle management, and integrate data engineering, feature management, and model serving into unified platform solutions. Champion automation of infrastructure provisioning, CI/CD pipelines, and production-quality software, while fostering technical excellence and collaboration with data scientists, ML engineers, product teams, and stakeholders.

Location: Palo Alto, CA, United States

Elevate your career by leading high-impact engineering teams and shaping the future of machine learning platforms at JPMorgan Chase, driving innovative solutions that empower data scientists and ML engineers across the organization.

As a Manager of Software Engineering at JPMorgan Chase in the Consumer and Community Banking Technology team, you will set strategic direction, oversee project delivery, and ensure alignment with business objectives for multiple engineering teams. Leveraging your leadership and technical expertise, you will guide the development of robust ML infrastructure and tools, foster a culture of technical excellence, and drive continuous improvement in platform capabilities. Your role will require exceptional collaboration and stakeholder management skills, as you empower teams, champion best practices, and represent the ML platform engineering function in cross-functional forums.

Job Responsibilities

Lead and manage engineering teams in the design, development, and maintenance of scalable machine learning platforms and infrastructure.

Set strategic direction for ML platform initiatives, ensuring alignment with business goals and enterprise standards.

Oversee the delivery of tools for model training, deployment, monitoring, and lifecycle management.

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

Ensure the implementation of secure, high-quality production code for platform services, APIs, and automation pipelines.

Collaborate with data scientists, ML engineers, product teams, and business stakeholders to define requirements and deliver impactful platform features.

Drive platform reliability, scalability, and performance through proactive monitoring, troubleshooting, and continuous improvement.

Oversee architecture and design documentation for platform components.

Champion automation of infrastructure provisioning, configuration, and CI/CD pipelines for ML platform services.

Foster a culture of technical excellence, innovation, and continuous learning within the engineering team.

Represent the ML platform engineering function in cross-functional forums and contribute to the community of practice.

Required Qualifications, Capabilities, and Skills

5+ years of applied experience or formal training/certification in software engineering concepts, including coaching and mentoring.

Proven experience building, deploying, and maintaining machine learning platforms or infrastructure.

Proficiency in Python and familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn).

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

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

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

Experience developing APIs and platform services for ML workflows.

Solid understanding of the software development life cycle, agile methodologies, and engineering best practices.

Demonstrated ability to lead and mentor engineering teams, and collaborate with cross-functional stakeholders.

Preferred Qualifications, Capabilities, and Skills

Experience 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.

Lead multiple teams, manage day-to-day implementation activities, and set overall guidance for team output, practices and collaboration

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