Software Engineer III - Machine Learning Platform

JPMorgan Chase & Co.•Palo Alto, CA

1d•$137,750 - $185,000

About The Position

We have an exciting and rewarding opportunity for you to take your software engineering career to the next level. As a Software Engineer III at JPMorganChase within the AI/ML data platform team you serve as a seasoned member of an agile team 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.

Requirements

Formal training or certification on 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 (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)
Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically 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.

Nice To Haves

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/WebDataset) 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.

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
Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
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.