Engineering Manager, Deep Learning Inference
$184,000–$287,500 year
RemoteSanta Clara, California, United States or New York, United States
Job Summary
Lead, mentor, and scale a high-performing engineering team focused on deep learning inference and GPU-accelerated software. Drive the strategy, roadmap, and execution of NVIDIA's inference frameworks engineering for Client AI, partnering with internal compiler, libraries, and research teams to deliver optimized inference pipelines. Oversee performance tuning, profiling, and optimization of large-scale models for LLM, multimodal, and generative AI applications, guiding engineers in adopting best practices for CUDA, Triton, CUTLASS, and multi-GPU communications. Represent the team in roadmap discussions to ensure alignment with NVIDIA's broader AI and software strategies while fostering a culture of technical excellence and continuous innovation.
Required Qualifications
- MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a related field
- 6+ overall years of software development experience
- 3+ years in technical leadership or engineering management
- Strong background in C/C++ software design and development
- Hands-on experience with GPU programming (CUDA, Triton, CUTLASS)
- Proven record of deploying or optimizing deep learning models in production environments
- Experience leading teams using Agile or collaborative software development practices
Desired Qualifications
- Proficiency in Python
- Significant open-source contributions to deep learning or inference frameworks such as PyTorch, vLLM / SGLang, Triton, or TensorRT-LLM
- Deep understanding of multi-GPU communications (NIXL, NCCL, NVSHMEM) and distributed inference architectures
- Expertise in performance modeling, profiling, and system-level optimization across CPU and GPU platforms
- Proven ability to mentor engineers, guide architectural decisions, and deliver complex projects with measurable impact
- Publications, patents, or talks on LLM serving, model optimization, or GPU performance engineering
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