Lead Software Engineer, Machine Learning Platform

JPMorgan Chase

Confirmed live yesterday High trust

Quick summary

Work type
On-site
Location
Palo Alto, CA
Posted
32 days ago
Freshness
Confirmed live yesterday

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Salary context

How this pay compares to similar roles

Similar $215k
$178k most similar roles pay here $245k

This listing doesn't post a salary. Most similar roles pay $192,050–$238,250.

Based on 240 similar postings.

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About JPMorgan Chase

JPMorgan Chase & Co. is a global financial services firm and one of the largest banks in the world, offering investment banking, commercial banking, asset management, and consumer financial services.

JPMorgan Chase currently has 1117 open roles on FindRole.

Listed pay typically runs $186,160–$215,000 across 7 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer, Machine Learning Platform

Lead Software Engineer - Machine Learning Platform joins the AI/ML Data Platforms team to build and operate scalable, reliable ML training systems and pipelines on AWS and other cloud platforms. This role involves productionizing GPU-based workloads, optimizing performance and cost efficiency, and enabling well-governed training across environments. Key responsibilities include designing end-to-end training platforms, managing Kubernetes infrastructure like EKS, and supporting Gen AI/LLM fine-tuning workflows with robust evaluation harnesses. The candidate will implement observability for training systems, improve developer experience through CI/CD and standardized containers, and lead the adoption of AI-assisted engineering practices. Required skills include Python, PyTorch or TensorFlow, distributed training concepts like DDP and DeepSpeed, and expertise in Kubernetes and AWS services. The role addresses technical challenges in GPU utilization, data loading bottlenecks, and large-scale dataset management for enterprise-grade machine learning infrastructure.

What you'll do

  • Design, build, and maintain end-to-end ML training platforms on AWS and other cloud environments.
  • Optimize GPU training workloads to improve throughput, utilization, and reproducibility for single-node and distributed systems.
  • Manage and operate training infrastructure on Kubernetes, including resource management and workload troubleshooting.
  • Enable Gen AI/LLM training workflows, including supervised fine-tuning and scalable execution patterns.
  • Implement observability for training systems through metrics, logs, dashboards, and operational runbooks.
  • Improve developer experience by providing standardized containers, CI/CD pipelines, and self-service workflows.
  • Drive the adoption of AI-assisted engineering practices to improve code quality and delivery speed.
  • Define infrastructure standards for security, access control, and cost management in collaboration with partner teams.

What we're looking for

  • Formal training or certification in software engineering concepts and 5+ years of applied experience.
  • Demonstrated experience running ML training in cloud environments and debugging infrastructure and code issues.
  • Strong Python skills with solid engineering practices like testing, code reviews, modular design, and dependency management.
  • Experience building automation/CI for ML codebases including build, test, release, and deployment workflows.
  • Hands-on experience with deep learning training workflows and at least one major framework such as PyTorch or TensorFlow.
  • Experience with distributed training concepts (e.g., DDP/FSDP/DeepSpeed) and profiling/optimizing training systems for CPU/GPU utilization.
  • Experience with Kubernetes fundamentals for compute-intensive workloads and AWS services like EKS, S3, IAM, and CloudWatch.
  • Demonstrated experience leading the use of approved AI-assisted software development tools and ensuring responsible AI practices.

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