Senior ML Platform Engineer: GPU Training & Cloud Pipelines

Next Frontier Capital; Palo Alto (CA); On-site; USD 170,000 - 250,000; Full time

Job summary

JPMorganChase, a leading global financial services firm, seeks a Lead Software Engineer to architect and operate scalable ML training pipelines on AWS and cloud platforms. You will optimize GPU workloads, ensure reproducibility, and drive enterprise-grade AI governance across environments.

You will collaborate with data engineering and platform teams to standardize interfaces, security, and cost controls, while improving 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.

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 training in cloud

Kubernetes

AWS

CI/CD

Code review

Tools

Kubernetes

AWS

EKS

CI/CD tooling

Job description

JPMorganChase, a leading global financial services firm, seeks a Lead Software Engineer to architect and operate scalable ML training pipelines on AWS and cloud platforms. You will optimize GPU workloads, ensure reproducibility, and drive enterprise-grade AI governance across environments.

You will collaborate with data engineering and platform teams to standardize interfaces, security, and cost controls, while improving developer experience with containers, CI/CD, and self-service workflows.