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.

Job 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

Required qualifications, capabilities, and skills

• 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

Preferred qualifications, capabilities, and skills

• 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.