Lead ML Platform Engineer for Scalable GPU Training

Next Frontier Capital; Palo Alto (CA); On-site; USD 180,000 - 245,000; Full time

Job summary

JPMorganChase in Palo Alto seeks a Lead Software Engineer to architect and operate scalable ML training platforms on AWS and other clouds. You will optimize GPU workloads, enable Gen AI workflows, and enforce governance with observability and automation across environments.

The role emphasizes enterprise-grade security, reproducibility, and efficient collaboration with data engineering and platform teams. A strong Linux+Python background and experience in cloud ML are essential.

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 (e.g., 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).
• 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 (EKS/ECR, S3, IAM, VPC...).
• Demonstrated experience leading effective use of approved AI-assisted software development tools with CI/CD and code reviews.
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity and secure handling.

Responsibilities

• Design, build, and maintain end-to-end ML training platform.
• Run and optimize GPU training workloads, improving throughput and reproducibility.
• Build and operate training infrastructure on Kubernetes and cloud platforms.
• Enable Gen AI/LLM training and fine-tuning workflows with governance and scalable GPU execution.
• Implement observability: metrics, logs, dashboards, alerting, and runbooks.
• Partner with data engineering and platform teams to define interfaces and guardrails.
• Improve developer experience: containers, CI/CD, templates, documentation, self-service workflows.
• Drive adoption of enterprise AI-assisted engineering practices for code quality and delivery speed.
• Apply SDLC toolchain knowledge to improve value from automation.

Skills

Python

ML in cloud

CI/CD for ML

DL frameworks (PyTorch/TensorFlow)

Distributed training

Kubernetes

AWS

Profiling & optimization

AI-assisted tooling

Responsible AI

Tools

Kubernetes

AWS

EKS

S3

IAM

Job description

JPMorganChase in Palo Alto seeks a Lead Software Engineer to architect and operate scalable ML training platforms on AWS and other clouds. You will optimize GPU workloads, enable Gen AI workflows, and enforce governance with observability and automation across environments.

The role emphasizes enterprise-grade security, reproducibility, and efficient collaboration with data engineering and platform teams. A strong Linux+Python background and experience in cloud ML are essential.