Principal Software Engineer - High Performance Computing

JPMorgan Chase & Co.; Palo Alto (CA); On-site; USD 210,000 - 260,000; Full time

Job summary

JPMorgan Chase & Co. in Palo Alto seeks a Principal Software Engineer to join the Core Foundational Platforms team.

You will provide engineering leadership, design and deliver secure, scalable technology products, and mentor other developers in an agile, high-performance environment. You will collaborate with data scientists, product partners, and engineers to optimize low-latency systems, establish telemetry-driven performance standards, and drive AI/ML initiatives across the firm while

Qualifications

• Formal training or certification on software engineering concepts and 10+ years applied experience.
• Hands-on experience delivering system design, application development, testing, and operational stability.
• Advanced knowledge of software development and technical processes with in-depth expertise in one or more disciplines (e.g. large language models, high-performance computing).
• 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.
• Strong programming skills in Python and at least one systems language (C or C++), plus experience with AI/ML frameworks such as PyTorch.
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices across teams, including setting standards for human-in-the-loop validation and secure data handling.
• Strong understanding of responsible AI use and control in engineering workflows; ability to influence senior technical leaders on safe scaling patterns.

Responsibilities

• Design and implement complex, scalable software frameworks using appropriate architecture patterns.
• Develop secure, high-quality production code; review, debug, and improve code written by other engineers.
• Lead technical design reviews and advise cross-functional partners on solutions within your domain of expertise.
• Create reusable performance patterns and benchmarking approaches to optimize model training and inference across compute architectures.
• Define and use telemetry and measurable performance indicators to guide hardware and software decisions.
• Mentor and enable the developer community in HPC practices that intersect with AI/ML.
• Architect and govern agentic AI-enabled engineering workflows to improve delivery speed and quality, while defining guardrails for validation and security across teams.
• Applies knowledge of SDLC tools and AI-assisted development capabilities to improve automation at scale.

Skills

Python

C/C++

PyTorch

CUDA

HPC

AI/ML frameworks

Large language models

System design

Telemetry

Education

PhD preferred

Tools

CUDA toolkit

Job description

As a Principal Software Engineer at JPMorganChase within the Core Foundational Platforms team, you provide expertise and engineering excellence as part of an agile team to enhance, build, and deliver trusted technology products in a secure, stable, and scalable way. You will collaborate with engineers, data scientists, and product partners, mentor other developers, and help teams make informed tradeoffs across low-latency, throughput, and efficiency - grounded in telemetry and operational outcomes.

Job responsibilities

• Design and implement complex, scalable software frameworks using appropriate architecture patterns
• Develop secure, high-quality production code; review, debug, and improve code written by other engineers
• Lead technical design reviews and advise cross-functional partners on solutions within your domain of expertise
• Create reusable performance patterns and benchmarking approaches to optimize model training and inference across compute architectures
• Define and use telemetry and measurable performance indicators to guide hardware and software decisions
• Mentor and enable the developer community in high-performance computing (HPC) practices that intersect with artificial intelligence/machine learning (AI/ML)
• Architects and governs agentic AI-enabled engineering workflows (using enterprise-authorized tools within the work environment) to improve delivery speed, code quality, and operational outcomes at scale (e.g., AI-driven PR review assistance, test generation/maintenance, release readiness checks, incident triage and root-cause acceleration), while defining guardrails for validation, security, resiliency, and reuse across teams.
• 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 at scale.

Required qualifications, capabilities and skills

• Formal training or certification on software engineering concepts and 10+ 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 in-depth expertise in one or more disciplines (for example, large language models, high-performance computing)
• 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
• Strong programming skills in Python and at least one systems language (C or C++), plus experience with AI/ML frameworks such as PyTorch
• Demonstrated experience designing and leading adoption of agentic AI-enabled development practices (using enterprise-authorized tools within the work environment) across teams, including setting standards for human-in-the-loop validation, auditability/traceability of changes, and secure handling of sensitive data.
• Strong understanding of responsible AI use and control expectations in engineering workflows, including security/resiliency implications, data sensitivity, and risk-based governance; ability to influence senior technical leaders on safe scaling patterns and reuse.

Preferred qualifications, capabilities and skills

• PhD preferred (Computer Science, Computer Engineering, Mathematics, or related discipline).
• Practical cloud native experience
• Hands-on experience with CUDA for GPU programming and performance optimization preferred
• Experience in large organizations and regulated industries is a plus
• Excellent communication skills and the ability to work collaboratively in a dynamic team environment