NVIDIA’s Edge AI, Metropolis, and Blueprints team seeks a hands-on Director of Systems Engineering to lead and develop world-class engineering and data teams across Europe, Asia, and the United States. This role involves architecting NVIDIA’s end-to-end inference acceleration strategy using TensorRT, VLLM, and other frameworks, while collaborating with OEMs to optimize custom deep learning models for Edge and Enterprise devices. The ideal candidate will have over 15 years of experience in machine learning and leadership roles, with expertise in GPU optimizations, large-scale data processing, and hands-on experience with multimodal AI systems. Strong communication skills and the ability to mentor teams are essential, as is a background in computer science or electrical engineering. This position requires deep knowledge of modern architectures like transformers and VLMs, along with proven success in delivering robust, low-latency inference solutions at scale for embedded and enterprise platforms.
Skills
TensorRT
VLLM
DeepStream
Python
C++
CUDA
MLPerf
Kubernetes
Docker
CI/CD
Git
NVIDIA GPUs
Heterogeneous computing
Multimodal AI
Large-scale data processing
Systems building
MLOps
CV
LLMs
VLMs
GenAI
What you'll do
Lead and develop world-class engineering and data teams across global locations.
Architect and operationalize NVIDIA’s end-to-end data Inference Acceleration strategy.
Drive strategic implementations of TensorRT and VLLM for accelerated inference solutions.
Collaborate with OEMs to architect custom deep learning models for specific requirements.
Offer direct customer support, including debugging and technical education for Metropolis partners.
Orchestrate performance benchmarking efforts on industry benchmarks like MLPerf.
Scale the team by hiring new members and mentoring existing teams for deep learning challenges.
What we're looking for
15+ years of experience with at least 10 years in machine learning/deep learning research or practical application.
Deep expertise in GPU, CPU, and dedicated deep learning architecture fundamentals, including low-level performance optimizations.
Hands-on experience with VLMs, LLMs, multimodal AI systems, and large-scale data processing pipelines.
Proven track record of delivering robust, low-latency inference at scale on NVIDIA GPUs and SOCs.
Strong technical leadership in architecting accelerated computing solutions for Edge and Enterprise devices.
Extensive experience leading global teams across multiple continents and time zones.
Bachelor’s or Master's degree in Computer Science/Electrical Engineering or equivalent industry experience.
Market check
Salary context
This
$320,000–$488,750
range sits above
100%
of similar postings on FindRole.
Peer median band
$152,000–$240,055
Median floor and ceiling across peers.
Typical midpoint (25–75%)
$165,000–$240,010
Middle half of comparable postings.
Based on 240 comparable postings.*
* 240 is the maximum number of comparable postings sampled.
Employer
About Nvidia
Nvidia is a leading designer of graphics processing units (GPUs) and system-on-chip units, powering gaming, professional visualization, data centers, and artificial intelligence workloads. Industry: Semiconductors & AI Computing
Nvidia currently has
801 open roles
on FindRole.
Listed pay typically runs
$184,000–$287,500
across 797 roles with salary data.