Lead Software Engineer - Machine Learning Platform

We have an opportunity to impact your career and provide an adventure where you can push the limits of what's possible.

As a Lead Software Engineer at JPMorganChase within AI/ML Data Platforms, you are an integral part of an agile team that works to build and operate scalable, reliable ML training systems and pipelines on AWS and other cloud platforms.

You will productionize training workloads (often GPU-based), improve performance and cost efficiency, and enable repeatable, well-governed training across environments.

Job 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.

Required qualifications, capabilities, and skills


* 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 ...