Jersey City, NJ, United States
6 hours ago
Principal Software Engineer - High Performance Computing

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, 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 frameworksDevelops secure and high-quality production code, and reviews and debugs code written by othersAdvises cross-functional teams on technological matters within your domain of expertiseServes as the function’s go-to subject matter expertContributes to the development of technical methods in specialized fields in line with the latest product development methodologiesCreates durable, reusable software frameworks that are leveraged across teams and functionsInfluences leaders and senior stakeholders across business, product, and technology teamsChampions the firm’s culture of diversity, opportunity, inclusion, and respectUsing telemetry, create measurable frameworks for deciding amongst hardware and software optionsPublish and support re-usable patterns to optimize training and inference of ML models on various architecturesSupport 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 experienceHands-on practical experience delivering system design, application development, testing, and operational stabilityExpert in one or more programming language(s)Advanced knowledge of software application development and technical processes with considerable in-depth knowledge in one or more technical disciplines (e.g., cloud, artificial intelligence, machine learning, mobile, etc.)Experience applying expertise and new methods to determine solutions for complex technology problems in one or more technical disciplinesExperience leading a product as a Product Owner or Product ManagerAbility to present and effectively communicate with Senior Leaders and ExecutivesUnderstanding of the businessPractical cloud native experienceExperience in Computer Science, Computer Engineering, Mathematics, or a related technical fieldFamiliarity with HPC software (e.g., NCCL, MPI) and resource schedulers (e.g., Kubernetes, SLURM)Preferred qualifications, capabilities, and skillsStrong programming skills in Python, scripting, C, C++ with experience in AI/ML frameworks like PyTorch and LangChainMaster’s Degree in Computer Science (required)8+ years of experience in high-performance computing software5+ years of experience with accelerators and deep learning, particularly large language modelsExperience in large organizations and regulated industries is a plusExcellent communication skills and the ability to work collaboratively in a dynamic team environmentPublish and support re-usable patterns to optimize training and inference of ML models on various architectures
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