Solution Architect, Financial Services
NVIDIA is a world leader in AI computing. Our innovations are transforming industries and powering the era of AI and accelerated computing. At NVIDIA, we believe in creating solutions that improve lives and contribute to a more sustainable future. Solutions Architects are trusted technical advisors to our customers; we work with the most exciting computing hardware and software, driving the latest breakthroughs in artificial intelligence!
We are looking for an individual who can enable customer productivity and develop lasting relationships with our technology partners, making NVIDIA an integral part of end-user solutions. Your duties will vary from working on proof-of-concept demonstrations to driving relationships with key technical leads and developers to ensure successful adoption of NVIDIA-based AI technology. Engaging with developers, quants, data scientists, and senior leaders is a significant part of the Solutions Architect role.
What you’ll be doing:
- FSI Strategic Engagement: Work with financial institutions and their technology ecosystem to leverage NVIDIA's advanced technologies.
- Algorithmic & SDK Guidance: Guide customers in implementing next-generation model distillation, domain adaptation, reinforcement learning (RL), and post-training algorithms by helping them adopt NVIDIA AI SDKs and APIs.
- Collaborative Innovation: Work closely with product management, engineering, applied research, and sales teams to develop and deliver comprehensive solutions.
- Technical Advocacy: Improve NVIDIA products and build creative solutions to overcome scaling challenges at the intersection of computer architecture, libraries, and AI applications.
- Knowledge Sharing: Contribute to the wider organization and community by sharing your expert knowledge, contributing to open-source projects, or delivering hands-on training.
- Enterprise Impact: Be part of the team that helps bring NVIDIA technology to life in the Enterprise, acting as the face and trusted expert advisor that our customers and partners rely on.
NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all.
What we need to see:
- BS, MS, or PhD degree in Machine Learning, Computer Science, Engineering, Mathematics, Physics, or a related technical field.
- Financial Services Background: Proven experience working within Financial Services firms.
- AI Framework Expertise: 5+ years of experience with AI frameworks such as PyTorch, JAX, or TensorFlow, and libraries like Hugging Face Transformers.
- Programming Proficiency: Strong Python programming, software design, and debugging skills.
- Parallelism & CUDA: Deep understanding of distributed computing and parallelism methodologies (model and data parallelism) coupled with a good understanding or hands-on experience with CUDA.
- Model Lifecycle: Experience with AI model lifecycle components, including pre-training, supervised fine-tuning, and model evaluation.
- Adaptability: Capable of working in a constantly evolving environment and quickly adapting to change.
- Communication: Clear written and oral communication skills with the ability to effectively collaborate with executives and engineering teams.
Ways to stand out from the crowd:
- Advanced CUDA: Deep expertise in writing and optimizing code for training and/or inference specifically for NVIDIA GPUs.
- NVIDIA AI Enterprise: Experience with or contributions to NVIDIA deep learning frameworks, particularly NeMo, Megatron Core, or NeMo-RL.
- Financial Workloads: Demonstrated understanding of how GPU acceleration can be applied specifically to financial workloads (e.g., algorithmic trading, risk modeling).
- Large-Scale Training: Hands-on experience in large-scale foundation model training, accuracy, and performance profiling.
- Tooling & Orchestration: Experience with distributed computing tools like SLURM and Kubernetes for training large models on GPUs.
