Lead Machine Learning Engineer-MLOps Vice President

JPMorgan Chase & Co. • Palo Alto, CA

12d • $171,000 - $260,000

About The Position

As Lead Machine Learning Engineer on the Recommendation Engine team, you will work closely with Data Scientists to build and deploy machine learning models using a modern MLOps stack. You will build and maintain pipelines for distributed model training on large compute clusters, batch and real-time model serving, large-scale hyperparameter tuning, model monitoring, production validation, and other activities that support model development, testing, and deployment in a well-managed, controlled environment.

The Personalization and Insights product builds and supports high-throughput, low-latency applications that use state-of-the-art machine learning architectures and are deployed in AWS. These applications power personalized experiences across Chase Consumer & Community Banking channels, combining traditional banking services with travel, merchant offer shopping, and dining services.

The CCB Data & Analytics team responsibly uses data across Chase to build business advantages while providing value and protection for customers. It includes disciplines spanning data governance and strategy, reporting, data science, and machine learning, and partners with Technology on data and analytics infrastructure.

Requirements

• BS in Computer Science or a related Engineering field with 6+ years of experience, or an MS degree in Computer Science or a related Engineering field with 4+ years of experience.
• Solid knowledge of and extensive experience with Python.
• Deep knowledge of and passion for data science fundamentals and training and deploying models.
• Experience with monitoring and observability tools to monitor model input/output and feature statistics.
• Operational experience with big data and machine learning tools such as Ray, DuckDB, and Spark.
• Solid grounding in engineering fundamentals and an analytical mindset.
• Action-oriented approach and iterative development.

Nice To Haves

• Experience with recommendation and personalization systems.
• Fundamentals and experience with containers, including the Docker ecosystem; container orchestration systems such as Kubernetes and ECS; and DAG orchestration such as Airflow and Kubeflow.
• Good knowledge of databases.
• Solid fundamentals in cloud computing, preferably AWS.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters to support scalable machine learning workflows.
• Develop and manage pipelines for high-throughput real-time inference and batch inference, ensuring optimal performance and reliability.
• Implement quantization techniques and deploy large language models (LLMs) to maximize efficiency and resource utilization.
• Oversee the management and optimization of vector databases for advanced AI and machine learning applications.
• Establish and maintain monitoring and observability pipelines to ensure system health, performance, and rapid issue resolution.
• Collaborate with cross-functional teams to integrate new technologies and continuously improve existing infrastructure.
• Partner with product, architecture, and other engineering teams to define scalable and performant technical solutions.

Benefits

• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.
• Competitive total rewards package, including base salary and, for eligible roles, commission-based pay and/or discretionary incentive compensation in cash and/or forfeitable equity.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior