Solutions Architect, Financial Services, Capital Markets

This is a fully remote position, open to applicants in California, +1 more state.

📋 Description

• Collaborate with NVIDIA's Engineering, Product, and Sales teams to achieve design wins with customers.

• Support the development and enhancement of NVIDIA product features through customer insights and proof-of-concept assessments.

• Conduct proof-of-concept projects in partnership with clients, engineers, and other architects, focusing on comprehensive analysis, profiling, and optimization of machine learning/deep learning models to guarantee optimal performance on both current and future GPU architectures.

• Engage directly with client ML researchers and developers/engineers on workflows, projects, and challenges that impact the business, facilitating success through NVIDIA technology.

• Ensure quick resolution of customer issues while fostering the highest levels of customer satisfaction.

• Create collateral (notebooks/blogs) relevant to Finance industry use cases, including ML/DL, recommender systems, GNN, Monte Carlo simulations, and Quantitative Finance, through close collaboration with customers.

⛳️ Requirements

• MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or other Engineering disciplines (or equivalent experience).

• Over 5 years of experience as an ML/Software Engineer with a solid track record in programming with Python and C++.

• Familiarity with ML/DL algorithms and frameworks such as PyTorch, Spark, Dask, Jax, and TensorFlow.

• Ability to articulate ideas and share code effectively through blog posts and GitHub.

• Enjoyment in collaborating with various teams and levels within organizations (engineering/research, product, sales, and marketing).

• Strong verbal and written communication skills along with technical presentation abilities.

• Self-motivated individual with a drive for growth, a genuine enthusiasm for continuous learning, and the willingness to share insights with the team.

• Proficiency in deploying ML/DL models at scale on public cloud computing platforms and/or on-premises HPC clusters in production environments.

🏝️ Benefits

• Equity

• Benefits