Senior Deep Learning Tools Engineer – CUDA Tile

at Nvidia

USD 152,000-241,500 per year

SENIOR

✅ On-site

Tech Stack
Tag name is followed by "@" symbol and proficiency level value. About proficiency levels:

1-2 — basic awareness. Minimal hands-on experience, and a rudimentary understanding of the technology's purpose;

3-6 — daily use. Comfortable and regular usage, capable of handling common tasks and challenges related to the technology;

7-9 — you are an expert, you can teach others, you know all the pitfalls and tricks;

10 — exceptional knowledge, comprehensive understanding, and adeptness in all aspects of the technology, including advanced problem-solving. Think twice before claiming or demanding such level.

AI CI/CD @ 4 CUDA @ 4 Data Analysis @ 6 Deep Learning @ 4 GPU @ 4 HPC JAX @ 4 LLVM @ 4 Mathematics @ 4 Performance Analysis @ 3 Profiling @ 6 PyTorch @ 4 Python @ 7 TensorFlow @ 4 TensorRT @ 4

Details

NVIDIA is building advanced compiler technologies to accelerate AI workloads. This role focuses on performance validation, analysis, and tracking at the intersection of deep learning compilers, GPU systems, and automation infrastructure. You will help ensure that performance improvements are measurable, scalable, and continuously validated over time.

You will collaborate with compiler developers, infrastructure providers, and hardware teams to build systems that track, analyze, and improve performance across rapidly evolving AI workloads.

Responsibilities

Design and develop performance testing frameworks for deep learning compilers and workloads.
Build and maintain automated CI/CD pipelines to continuously track performance across models, hardware, and compiler changes.
Implement benchmarking systems to measure latency, throughput, and efficiency of AI and HPC workloads.
Analyze performance trends over time and identify regressions, bottlenecks, and optimization opportunities.
Partner with compiler and architecture teams to debug and resolve performance issues.
Develop tools and dashboards for performance visualization, reporting, and insights.
Enable scalable testing across diverse GPU systems and environments.
Improve infrastructure to ensure reliable, reproducible, and high-signal performance data.

Requirements

Bachelor's, master's, or PhD degree, or equivalent experience, in Computer Science, Computer Engineering, Electrical Engineering, Mathematics, or a related field.
5+ years of software engineering experience, including performance engineering, benchmarking, or systems optimization.
Strong programming skills in Python; C++ is a plus.
Experience with CI/CD systems and automation frameworks.
Familiarity with hardware-aware performance analysis, including GPUs, accelerators, or similar systems.
Experience with deep learning frameworks such as PyTorch, TensorFlow, JAX, or TensorRT.
Background in data analysis, profiling, and regression tracking.
Ability to debug complex system-level issues across software and hardware layers.

Preferred Qualifications

Experience with GPU performance analysis and optimization.
Understanding of compiler internals, including LLVM, MLIR, and CUDA compilation flow.
Experience building performance dashboards and large-scale telemetry systems.
Familiarity with hardware/software co-design or low-level performance tuning.
Experience with distributed testing infrastructure or large-scale benchmarking systems.

Compensation and Benefits

Base salary range: $152,000–$241,500 USD per year.
Eligibility for equity and benefits.
NVIDIA is an equal opportunity employer committed to fostering a diverse work environment.

Applications will be accepted at least until May 10, 2026. This posting is for an existing vacancy. NVIDIA uses AI tools in its recruiting processes.