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 JPMorgan

Chase 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 product ionize training workloads (often GPU-based), improve performance and cost efficiency, and enable repeatable, well-governed training across environments.

• 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.

• 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 Tensor Flow).
• 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/Deep Speed 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.

• Experience running training workloads across multiple cloud platforms and managing portability, performance, and governance across environments.
• Familiarity with cloud-native networking/storage patterns for high-throughput training and artifact management.
• Experience optimizing training input pipelines (sharding, prefetching, caching, format choices such as Parquet/Web Dataset) and working with large datasets.
• Familiarity with distributed compute frameworks (Spark, Ray, Dask) for feature/dataset generation.
• Familiarity with workflow orchestration tools (Airflow-like systems, Argo Workflows-like patterns) and model registry concepts.
• Experience optimizing training cost/performance (right-sizing, scheduling policies, interruptible capacity strategies where applicable budge guardrails, quota planning).
• Strong observability practice for training systems: metrics/logs/traces, GPU telemetry, dashboards, and alert tuning.

FEDERAL DEPOSIT INSURANCE ACT:
This position is subject to Section 19 of the Federal Deposit Insurance Act. As such, an employment offer for this position…