1 day ago
Base Salary
$200k - $322k/yr
Responsibilities
- Develop and own agentic systems from finance-partner problem scoping through evaluation harnesses, retrieval quality, cost management, and reliable production operation.
- Set architecture, shared primitives, engineering patterns, and quality standards for AI systems.
- Mentor engineers and represent AI engineering to Finance and NVIDIA leadership.
- Evaluate new models, agent frameworks, retrieval techniques, and tooling through hands-on experiments and build/no-build decisions.
- Partner with the model team to leverage, fine-tune, and post-train NVIDIA Nemotron models for finance use cases.
Requirements
- Master's or PhD, or equivalent experience, in computer science, engineering, data science, or a related technical field.
- 12+ years of experience building and operating production systems, including 3+ years with LLM or agent systems.
- Track record as a core technical contributor who has shipped and operated multiple LLM-powered applications or agents.
- Hands-on experience with skill-based workflows, harnesses, and agent orchestration frameworks.
- Advanced retrieval expertise and deep hands-on experience designing evaluations and observability for LLM systems.
- Strong Python, engineering fundamentals, agentic and adversarial software engineering experience, understanding of LLM security and safety risks, and practical knowledge of networks and operating systems.
- Ability to influence without authority and align engineers and business leaders on technical direction.
- Preferred qualifications include experience with NVIDIA's applied AI stack, recognized applied AI expertise, large technology company or finance-adjacent experience, and depth in fine-tuning and reinforcement learning.
Benefits
- Competitive salary, equity, and comprehensive benefits.
- Applications will be accepted at least until October 30, 2026.
Tech Stack
Categories
About Nvidia
Nvidia designs and sells GPUs and accelerated computing platforms for data centers, AI/ML, graphics, gaming, and automotive, monetizing through hardware, software platforms (CUDA, AI frameworks), and systems like DGX and networking. Customers include cloud providers, enterprises, researchers, and OEMs. Founded in 1993 and headquartered in Santa Clara, it is a public company traded on NASDAQ under NVDA.