Lead ML Platform Engineer — GPU Training on Cloud

JPMorgan Chase & Co.; Palo Alto (CA); On-site; USD 180,000 - 270,000; Full time

Job summary

JPMorganChase in Palo Alto is seeking a Lead Software Engineer to drive the design, build, and operation of scalable ML training systems on AWS and other clouds. You will optimize GPU workloads, enable Gen AI/LLM training, and establish governance and observability across environments.

You will partner with data engineering and platform teams to define interfaces, ensure security and cost controls, and improve developer experience with containers, CI/CD, and self-service workflows.

Qualifications

• Formal training or certification on software engineering concepts and 5+ years applied experience.
• Demonstrated experience running ML training in cloud environments and debugging issues across infrastructure & code.
• Strong Python skills with solid engineering practices (testing, code reviews, modular design, dependency management).
• Experience building automation/CI for ML codebases (build, test, release, deployment/promotion workflows).
• Hands on experience with deep learning training workflows and at least one major framework (eg., PyTorch or TensorFlow).
• Understanding of training performance and stability: data loading bottlenecks, mixed precision, checkpointing, reproducibility, and evaluation methodology.
• Experience with distributed training and related concepts (e.g., DDP/FSDP/DeepSpeed concepts, collective communication basics, scaling and bottleneck analysis).
• Ability to profile and optimize training systems (CPU/GPU utilization, memory, I/O throughput, networking, scheduling).
• Experience with Kubernetes fundamentals for running compute-intensive workloads and AWS (eg., EKS/ECR, S3, IAM, VPC/networking, Cloudwatch, EC2)
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices.

Responsibilities

• Design, build, and maintain end-to-end ML training platform.
• Run and optimize GPU training workloads (single-node and distributed), improving throughput, utilization and reproducibility.
• Build and operate training infrastructure on Kubernetes (e.g., EKS and other manage Kubernetes platforms), including resource management and workload troubleshooting.
• Enable Gen AI/LLM training and fine-tuning workflows (e.g., supervised fine-tuning), including evaluation harnesses, artifact/version governance, and scalable GPU execution patterns aligned to enterprise controls.
• Implement observability for training systems: metrics, logs, dashboards, alerting, and operational runbooks.
• Partner with data engineering and platform teams to define interfaces, standards, and guardrails (security, access, cost controls)
• Improve developer experience for training: standardized containers, CI/CD, templates, documentation, and self-service workflow
• Drives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.

Skills

Python

ML CI/CD

PyTorch

TensorFlow

Kubernetes

AWS

Distributed training

GPU training

Profiling

Observability

Security & compliance

Tools

Kubernetes (EKS)

S3

IAM

CloudWatch

EC2

Job description

JPMorganChase in Palo Alto is seeking a Lead Software Engineer to drive the design, build, and operation of scalable ML training systems on AWS and other clouds. You will optimize GPU workloads, enable Gen AI/LLM training, and establish governance and observability across environments.

You will partner with data engineering and platform teams to define interfaces, ensure security and cost controls, and improve developer experience with containers, CI/CD, and self-service workflows.