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Senior Solution Engineer, AI Enterprise
NVIDIA
New York, NYOn Site
Original Job Summary
Overview
NVIDIA is looking for an engineer who thrives at the intersection of innovative technology and real-world customer applications. As a Senior Solution Engineer, AI Enterprise, you will support the NVIDIA AI Enterprise product line, working directly with customers to address datacenter-scale AI/ML workloads, resolve issues, and contribute to evolving products and support software.
Key Responsibilities
- Support NVIDIA Enterprise customers by addressing technical issues.
- Collaborate with cross-functional engineering teams for root-cause analysis.
- Contribute to product improvements through coding, debugging, and design feedback.
- Own and drive customer issues from inception to resolution.
- Document customer interactions to build a robust knowledge base.
- Mentor early-career engineers and lead strategic projects.
- Occasional weekend and holiday support for customers.
Qualifications
- BS in Computer Science, Electrical Engineering, or equivalent.
- 8+ years engineering experience with 5+ years in leadership.
- Expertise in AI/ML training, inference, and real-world deployments.
- Deep understanding of Linux environments and optimization for AI/ML.
- Proficiency in Python and C/C++, with experience in containerized solutions.
- Experience with Docker, Kubernetes, and Slurm.
- Strong communication skills tailored to varied audiences.
Preferred Skills
- GPU acceleration and parallel programming (e.g., CUDA).
- Experience with cloud and virtualized environments.
- Familiarity with distributed workloads and HPC technologies.
Compensation & Benefits
Competitive base salary, equity, and benefits. Base salary ranges are provided based on level with applications accepted until August 8, 2025.
Key skills/competency
- AI
- ML
- Linux
- Python
- C/C++
- Containerization
- GPU
- Debugging
- Mentoring
- Enterprise
How to Get Hired at NVIDIA
🎯 Tips for Getting Hired
- Customize resume: Highlight AI/ML and leadership experiences.
- Demonstrate expertise: Emphasize Linux, Python, and containerization.
- Research NVIDIA: Understand their products and engineering culture.
- Prepare examples: Showcase complex problem-solving and project leadership.
📝 Interview Preparation Advice
Technical Preparation
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Review AI/ML frameworks and Linux optimization.
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Practice Python and C/C++ coding challenges.
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Study container orchestration with Docker and Kubernetes.
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Research GPU acceleration techniques and CUDA basics.
Behavioral Questions
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Describe a challenging project you led.
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Explain handling high-pressure customer situations.
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Discuss mentoring experiences with junior engineers.
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Outline strategies for cross-team collaboration.