JPMorgan Chase
Principal Software Engineer - High Performance Computing
$204K–$285K
Palo Alto, CA +2 · Principal · Full-time · Posted Aug 27, 2026
- Location
- Palo Alto, CA
Seattle, WA
Jersey City, NJ - Experience
- 10+ years applied experience; 8+ years in HPC software; 5+ years with at least one type of accelerator; 3+ years in deep learning, particularly large language models.
- Education
- Formal training or certification on software engineering concepts; PhD preferred (Computer Science, Computer Engineering, Mathematics, or related discipline).
About the job
Leads HPC software engineering and performance optimization for AI/ML workloads, while establishing agentic AI-enabled engineering practices and governance across teams.
What you'll do
- Design, build, review, and improve secure, scalable software frameworks and production code.
- Create benchmarking and telemetry practices to optimize model training and inference across compute architectures.
- Lead technical reviews, mentor engineers, and govern agentic AI-enabled development workflows with appropriate safeguards.
What they ask for
- Formal training or certification in software engineering concepts and 10+ years of applied experience.
- 8+ years of HPC software experience, 5+ years with at least one type of accelerator, and 3+ years in deep learning, particularly large language models.
- Strong Python and at least one systems language (C or C++), plus experience with AI/ML frameworks such as PyTorch.
- Experience leading adoption of agentic AI-enabled development practices across teams, including validation, auditability, and secure data handling.
Nice to have
- PhD in Computer Science, Computer Engineering, Mathematics, or a related discipline.
- Practical cloud native experience; hands-on CUDA experience for GPU programming and performance optimization.
Pay
USD 204,250.00-$285,000.00 base salary (Palo Alto, CA); USD 204,250.00-$285,000.00 base salary (Seattle, WA); USD 204,250.00-$285,000.00 base salary (Jersey City, NJ)
Tools
Python, C, C++, PyTorch, CUDA
AI focus
Optimizes AI/ML model training and inference across compute architectures and leads agentic AI-enabled engineering workflows, including setting validation, security, resiliency, auditability, and governance guardrails.
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