Solutions Architect, Financial Services Capital Markets

The Financial Services Solution Architect team is looking for an extraordinary person to join an experienced team of Quants and Data Scientists, engaging the finance industry with compelling examples of full-stack accelerated computing. Solution Architects work with top minds with Financial Services Capital Markets and Exchange firms to accelerate High-Performance Computing and AI workloads across various use cases. We’re seeking an inquisitive, hard-working, and creative individual passionate about helping solve challenges. Join us in this endeavor!What You’ll Be Doing:Partner with NVIDIA Engineering, Product, and Sales teams to secure design wins at customers. Enable development and growth of NVIDIA product features through customer feedback and proof-of-concept evaluations.Perform proof-of-concepts working side by side with clients, engineers, and other architects on in-depth analysis, profiling and optimization of machine learning/deep learning models to ensure the best performance on current- and next-generation GPU architectures.Work directly with client ML researchers and developers/engineers on business-impacting workflows, projects, and issues to drive success using NVIDIA technology.Facilitate rapid resolution of customer issues and promote the highest levels of customer satisfaction.Build collateral (notebooks/ blogs) applied to Finance industry use-cases such as ML/DL, recommender systems, GNN, monte-carlo simulations, Quantitative Finance, etc. by working closely with customers.What We Need To See:MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or other Engineering fields (or equivalent experience)5+ years experience as an ML/Software Engineer with a proven track record in writing code in Python, C++Experience with ML/DL algorithms with frameworks such as PyTorch, Spark, Dask, Jax, TensorFlowAbility to communicate ideas and share code clearly through blog posts, GitHubEnjoy working with multiple levels and teams across organizations (engineering/research, product, sales, and marketing teams)Effective verbal/written communication and technical presentation skillsSelf-starter with a passion for growth, a real enthusiasm for continuous learning, and sharing findings across the teamSkilled in deploying ML/DL models at scale on public cloud computing and/or on-prem HPC clusters in productionWays To Stand Out From The Crowd:Demonstrate C/C++ programming proficiency with an understanding of software design, programming techniques, and algorithms, alongside experience performing performance optimizations.Familiarity with NVIDIA GPU architectures.GPU Development experience through NVIDIA CUDA-x libraries, cuBLAS, cuDNN.Knowledge of MLOps technologies such as containers, data center deployments, cluster management software, etc.Experience working with enterprise developers building HPC or data analytics applications.NVIDIA is widely considered to be one of the technology world’s most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us. If you're creative and autonomous, we want to hear from you!Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 152,000 USD - 241,500 USD.You will also be eligible for equity and benefits.Applications for this job will be accepted at least until June 15, 2026.This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.SummaryLocation: US, NY, Remote; US, CA, Santa ClaraType: Full time