Engineering Manager, Deep Learning Inference
At a glance
Manager-level Machine Learning Engineering role at NVIDIA. US, CA, Santa Clara · full-time.
In listing Machine Learning Engineering salary
aimlroles summary, based on the employer's posting.
What you'll do
- Lead and grow engineers building software for deep-learning inference and GPU-accelerated deployment
- Set the technical direction and delivery plan for inference frameworks, with a focus on Client AI
- Coordinate with compiler, library and research groups to build optimized inference pipelines for NVIDIA accelerators
- Guide performance work on large language, multimodal and generative AI models, including profiling and tuning
- Represent your team in planning and steer engineering practices for CUDA, Triton, CUTLASS and multi-GPU communication
What you bring
- A master's degree, doctorate or equivalent background in computer science, computer or electrical engineering, or a related area
- At least six years in software development, including three years in technical leadership or engineering management
- Strong C/C++ development skills and practical GPU programming experience with CUDA, Triton or CUTLASS
- A record of deploying or improving deep-learning models in production, plus experience leading Agile or collaborative teams
Who this fits
This manager role suits a technical leader who can guide engineers while staying close to GPU programming and inference performance. You’ll work across engineering and research teams, and lead work centered on Client AI.
From the employer
NVIDIA is seeking an exceptional Manager, Deep Learning Inference Software, to lead a world-class engineering team advancing the state of AI model deployment. You will shape the software powering today’s most sophisticated AI systems — from large language models to multimodal generative AI — all accelerated on NVIDIA GPUs. The Deep Learning Inference team develops and optimizes open-source frameworks that make AI deployment scalable, efficient, and accessible — including vLLM / SGLang, and FlashInfer. Our work enables developers worldwide to harness NVIDIA accelerators for real-time inference at every scale, from datacenter clusters to edge devices.
What you'll be doing:
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, focusing on Client AI.
Partner with internal compiler, libraries, and research teams to deliver end-to-end optimized inference pipelines across NVIDIA accelerators.
Oversee performance tuning, profiling, and optimization of large-scale models for LLM, multimodal, and generative AI applications.
Guide engineers in adopting best practices for CUDA, Triton, CUTLASS, and multi-GPU communications (NIXL, NCCL, NVSHMEM).
Represent the team in roadmap and planning discussions, ensuring alignment with NVIDIA’s broader AI and software strategies.
Foster a culture of technical excellence, open collaboration, and continuous innovation.
What we need to see:
MS, PhD, or equivalent experience in Computer Science, Electrical/Computer Engineering, or a related field.
6+ overall years of software development experience, including 3+ years in technical leadership or engineering management.
Strong background in C/C++ software design and development; proficiency in Python is a plus.
Hands-on experience with GPU programming (CUDA, Triton, CUTLASS) and performance optimization.
Proven record of deploying or optimizing deep learning models in production environments.
Experience leading teams using Agile or collaborative software development practices.
Ways to Stand out from The Crowd:
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.
With highly competitive salaries and a comprehensive benefits package, NVIDIA is widely considered to be one of the technology world's most desirable employers. We have some of the most forward-thinking and hardworking people in the world working for us and, our rapid growth means endless opportunities for career advancement. If you’re a passionate technical leader ready to shape the future of AI inference frameworks — and build the software that powers the world’s most advanced models — we’d love to hear from you.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. The base salary range is 184,000 USD - 287,500 USD for Level 2, and 224,000 USD - 356,500 USD for Level 3.You will also be eligible for equity and benefits.
Applications for this job will be accepted at least until August 9, 2026.This posting is for an existing vacancy.
NVIDIA uses AI tools in its recruiting processes.
NVIDIA is committed to fostering an inclusive work environment and proud to be an equal opportunity employer. As we highly value diversity in our current and future employees, we do not discriminate (including in our hiring and promotion practices) on the basis of race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law.