Principal Software Engineer - High Performance Computing

JPMorganChase•Seattle, WA

About The Position

If you are looking for a game-changing career, working for one of the world's leading financial institutions, you’ve come to the right place. As a Principal Software Engineer at JPMorganChase within the Core Foundational Platforms team, you provide expertise and engineering excellence as an integral part of an agile team to enhance, build, and deliver trusted market-leading technology products in a secure, stable, and scalable way. Leverage your advanced technical capabilities and collaborate with colleagues across the organization to drive best-in-class outcomes across various technologies to support one or more of the firm’s portfolios. You will lead virtual and direct teams of developers, teaching them best practices in high-performance computing (HPC) practices that intersect with AI/ML. Thus, you are collaborative—especially since you will work closely with cross-functional teams comprised of data scientists, business analysts and other engineers. You will infuse the JPMorgan developer community with an appreciation of the impact that HPC can have by delivering software that consistently outperforms other platforms. You will deliver a variety of options to serve our various business needs--sometimes driven by low-latency; other times driven by throughput or low power.

Requirements

Formal training or certification on software engineering concepts and 7+ years applied experience
Hands-on practical experience delivering system design, application development, testing, and operational stability
Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g. LLMs, High-performance computing (HPC), etc.)
Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplines
Ability to present and effectively communicate with Senior Leaders and Executives
Master’s Degree in Computer Science
8+ years of experience in high-performance computing software
5+ years of experience with at least one type of accelerator
3+ years of experience in deep learning, particularly large language models
Deep familiarity with HPC software (e.g., NCCL, MPI) and resource schedulers (e.g., Kubernetes, SLURM)
Strong programming skills in Python, scripting, C, C++ with experience in AI/ML frameworks like PyTorch and LangChain

Nice To Haves

PhD preferred (Computer Science, Computer Engineering, Mathematics, or related discipline).
Practical cloud native experience
Experience in large organizations and regulated industries is a plus
Hands-on experience with CUDA for GPU programming and performance optimization preferred.
Excellent communication skills and the ability to work collaboratively in a dynamic team environment
Publish and support re-usable patterns to optimize training and inference of ML models on various architectures
Experience in high‑performance computing within national labs or academic research environments, including large‑scale distributed systems and accelerator‑based workloads.

Responsibilities

Creates complex and scalable coding frameworks using appropriate software design frameworks
Develops secure and high-quality production code, and reviews and debugs code written by others
Advises cross-functional teams on technological matters within your domain of expertise
Serves as the function’stechnical, go-to subject matter expert
Contributes to the development of technical methods in specialized fields in line with the latest product development methodologies
Creates durable, reusable software frameworks that are leveraged across teams and functions
Influences leaders and senior stakeholders across product, data science and technology teams
Champions the firm’s culture of diversity, opportunity, inclusion, and respect
Using telemetry, create measurable frameworks for deciding amongst hardware and software options
Publish and support re-usable patterns to optimize training and inference of ML models on various architectures
Support developer community in learning lessons from high-performance computing (HPC) domain