Lead ML Engineer - Scalable AI Platforms

J.P. Morgan; New York (NY); On-site; USD 150,000 - 230,000; Full time

Job summary

J.P. Morgan is seeking a Lead Machine Learning Engineer for the Digital Intelligence team in New York. You will build and maintain pipelines for distributed training, develop model promotion workflows, and optimize throughput on GPU clusters.

Collaboration across product and engineering teams will power Chase's digital channels with scalable ML platforms. The role combines deep ML expertise with software engineering, data science fundamentals, and observability to deliver reliable and secure

Qualifications

• BS in Computer Science or related Engineering with 6+ years, or MS with 4+ years.
• Strong Python and cloud computing knowledge with ML frameworks (PyTorch, TensorFlow).
• Deep knowledge of data science fundamentals, training and deploying models.
• Experience with monitoring/observability tools for model input/output and features.
• Experience with Ray, Spark, and training/inference systems like Ray, vllm/SGLang.
• Solid grounding in engineering fundamentals and enterprise system design.

Responsibilities

• Build, deploy, and maintain robust pipelines for distributed training on GPU-enabled clusters.
• Develop and manage pipelines for model promotion and MDLC capabilities.
• Optimize training throughput for large data sources.
• Establish and maintain integrations to platforms and tools related to model monitoring and observability.
• Collaborate with cross-functional teams to integrate new technologies and improve ML Platform capabilities.
• Partner with product, architecture, modeling, and engineering to design robust solutions powering Digital channels.

Skills

Python

Cloud computing

PyTorch

TensorFlow

Monitoring tools

Ray

Spark

SGLang

Education

Bachelor's or MS in CS/Engineering

Tools

Docker

Kubernetes

Airflow

Kubeflow

Job description

J.P. Morgan is seeking a Lead Machine Learning Engineer for the Digital Intelligence team in New York. You will build and maintain pipelines for distributed training, develop model promotion workflows, and optimize throughput on GPU clusters.

Collaboration across product and engineering teams will power Chase's digital channels with scalable ML platforms. The role combines deep ML expertise with software engineering, data science fundamentals, and observability to deliver reliable and secure