Principle Engineer - HPC Architect

Wells Fargo is seeking a Principal Engineer.

• Act as an advisor to leadership to develop or influence applications, network, information security, database, operating systems, or web technologies for highly complex business and technical needs across multiple groups
• Lead the strategy and resolution of highly complex and unique challenges requiring in-depth evaluation across multiple areas or the enterprise, delivering solutions that are long-term, large-scale and require vision, creativity, innovation, advanced analytical and inductive thinking
• Translate advanced technology experience, an in-depth knowledge of the organizations tactical and strategic business objectives, the enterprise technological environment, the organization structure, and strategic technological opportunities and requirements into technical engineering solutions
• Provide vision, direction and expertise to leadership on implementing innovative and significant business solutions
• Maintain knowledge of industry best practices and new technologies and recommends innovations that enhance operations or provide a competitive advantage to the organization
• Strategically engage with all levels of professionals and managers across the enterprise and serve as an expert advisor to leadership

• 7+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education.

• 7+ years building distributed systems and platforms.
• Deep expertise in distributed computing (Kubernetes, Spark, Ray, Dask, distributed schedulers, HPC, large-scale data platforms).
• Strong CUDA/GPU programming experience, including performance tuning and GPU orchestration.
• Experience with cloud-native platforms (Azure preferred), containers, orchestration, and infrastructure automation.
• Demonstrated principal-level influence with the ability to define technology strategy and drive adoption across multiple engineering organizations.

• Architect and build large-scale distributed computing platforms that power model development, training, execution, and risk analytics workloads across cloud and hybrid environments, delivering high performance, resiliency, scalability, and operational excellence.
• Lead GPU-accelerated computing strategy by designing and optimizing CUDA-based workloads, distributed GPU scheduling, high-performance data processing, and model execution frameworks to maximize utilization and reduce compute costs.
• Drive technical direction for PyFARM and related model platforms, establishing architecture standards, platform patterns, observability, reliability engineering practices, and long-term roadmaps for model risk and quantitative analytics ecosystems.
• Partner with quantitative researchers, model developers, data scientists, and platform engineering teams to enable secure, governed, and efficient execution of large-scale machine learning, simulation, and analytical workloads while ensuring regulatory and risk-management requirements are met.
• Serve as a technical leader and multiplier across the organization, mentoring senior engineers, leading critical design reviews, influencing enterprise architecture decisions, and driving innovation in distributed systems, high-performance computing, AI infrastructure, and data-intensive platforms.