Senior ML Platform Engineer - GPU & Cloud Pipelines

Next Frontier Capital; Palo Alto (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

JPMorganChase is seeking a Software Engineer III to design and operate an end-to-end ML training platform on AWS and other clouds. You will run GPU workloads, optimize performance, and enable Gen AI workflows within a governed, secure environment.

You will collaborate with data engineering and platform teams, improve developer experience, and implement observability with metrics, logs, and dashboards across the training stack.

Qualifications

• Formal training or certification in software engineering concepts and 3+ 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 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)
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment with demonstrated ability to evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.

Responsibilities

• Design, build, and maintain end-to-end ML training platform.
• Run and optimize GPU training workloads (single-node and distributed).
• Build and operate training infrastructure on Kubernetes (e.g., EKS) including resource management and troubleshooting.
• Enable Gen AI/LLM training and fine-tuning workflows with evaluation harnesses and governance.
• Implement observability for training systems: metrics, logs, dashboards, alerting, runbooks.
• Partner with data engineering and platform teams to define interfaces, guardrails (security, cost controls).
• Improve developer experience for training: containers, CI/CD, templates, documentation, self-service workflow.
• Leverage AI-assisted development tools and ensure outputs are correct, secure, and well-tested.

Skills

Python

ML training in cloud

Kubernetes familiarity

AWS cloud concepts

Performance optimization

CI/CD for ML

Debugging infrastructure

GPU training workflows

Education

Formal training or certification in software engineering concepts

Tools

Kubernetes (EKS)

AWS (S3, IAM, EC2, ECR)

GPU compute

PyTorch/TensorFlow

Observability tools

Job description

JPMorganChase is seeking a Software Engineer III to design and operate an end-to-end ML training platform on AWS and other clouds. You will run GPU workloads, optimize performance, and enable Gen AI workflows within a governed, secure environment.

You will collaborate with data engineering and platform teams, improve developer experience, and implement observability with metrics, logs, and dashboards across the training stack.