Nvidia Forward Deployed Engineer AI Accelerator 

United States, Texas 

953926841

What you will be doing:

In this role, you will implement innovative solutions that push the boundaries of what's possible with AI infrastructure while directly impacting customer success with breakthrough AI initiatives!

• Technical Implementation: Design and deploy custom AI solutions including distributed training, inference optimization, and MLOps pipelines across customer environments

• Customer Support: Provide remote technical support to strategic customers, optimize AI workloads, diagnose and resolve performance issues, and guide technical implementations through virtual collaboration

• Infrastructure Management: Deploy and manage AI workloads across DGX Cloud, customer data centers, and CSP environments using Kubernetes, Docker, and scheduling systems for GPU

• Performance Optimization: Profile and optimize large-scale model training and inference workloads, implement monitoring solutions, and resolve scaling challenges

• Integration Development: Build custom integrations with customer systems, develop APIs and data pipelines, and implement enterprise software connections

• End-user Documentation: Create implementation guides, documentation for resolution approaches and standard methodologies for complex AI deployments

What we need to see:

• 8+ years of experience in customer-facing technical roles (Solutions Engineering, DevOps, ML Infrastructure Engineering)

• BS, MS, or Ph.D. in CS, CE, EE (related technical field) or equivalent experience.

• Strong proficiency with Linux systems, distributed computing, Kubernetes, and GPU scheduling

• AI/ML experience supporting inference workloads and training at large-scale

• Programming skills in Python, with experience in PyTorch, TensorFlow, or similar AI frameworks

• Customer engagement ability to work effectively with technical teams under high-pressure situations

Ways to stand out from the crowd:

• NVIDIA ecosystem experience with DGX systems, CUDA, NeMo, Triton, or NIM

• Cloud platforms hands-on experience with AWS, Azure, or GCP AI services

• MLOps expertise with containerization, CI/CD pipelines, and observability tooling

• Infrastructure as code experience with Terraform, Ansible, or similar automation tools

• Enterprise software integration experience with Salesforce, ServiceNow, or similar platforms