NVIDIA
Forward Deployed Engineer - Finance
$116K–$230K
US, CA, Santa Clara · Full-time · Posted Oct 28, 2026
- Experience
- 3+ years in computer science, data science/engineering, analytics, automation, or applied AI.
- Education
- BS or MS degree in Data Science, Computer Science, Engineering, Economics, Finance, Business Analytics, or a related field, or equivalent experience.
About the job
Enables NVIDIA Finance teams to adopt AI by translating finance problems into secure, production-ready solutions and supporting their sustained use.
What you'll do
- Translate finance workflows into technical designs, success measures, and delivery plans; guide solution architecture.
- Prototype and productionize AI agents, retrieval-augmented generation, workflow automation, analytics, and ML applications.
- Lead initiatives from discovery to launch, integrate data securely, and develop reusable architectures and enablement assets.
What they ask for
- Hands-on Python, SQL, and Git, with Databricks or a comparable cloud data and AI platform.
- Practical experience building and deploying modern AI, including LLMs, prompt/context design, agents, MCPs, and evaluations; API integration across structured and unstructured data.
- Product and program judgment, plus working knowledge of responsible AI, privacy, security, and governance; strong communication skills.
- 3+ years in computer science, data science/engineering, analytics, automation, or applied AI.
Nice to have
- Applied AI, automation, or advanced analytics in finance-related areas; agentic workflows connected securely to systems such as SAP, SFDC, or Workday.
- Experience building communities of practice and teaching technical audiences; experience in a large global technology company or complex, high-growth environment.
Pay
116,000 USD - 184,000 USD (base salary, Level 2); 152,000 USD - 230,000 USD (base salary, Level 3)
Tools
Python, SQL, Git, Databricks, LLMs, MCPs, SAP, SFDC, Workday
AI focus
Build and deploy AI agents, retrieval-augmented generation, workflow automation, analytics, and ML applications; enable AI adoption across Finance.
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