Senior AI and Quant DevTech Engineer

at Nvidia

USD 152,000-287,500 per year

SENIOR

✅ Hybrid

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 @ 7 Algorithms @ 6 CUDA @ 7 Communication @ 7 GPU @ 7 HPC LLM @ 4 Machine Learning @ 4 Mathematics Parallel Programming @ 7 Prioritization @ 7 TensorRT @ 4

Details

We are looking for a software engineer with a strong background in parallel processing and GPU architecture to push the limits of performance at the intersection of AI, high-performance computing, and financial markets. In this role, you will work with parallel algorithms, GPUs, and sophisticated systems to identify and eliminate bottlenecks and unlock the capabilities of advanced processing hardware.

You will collaborate with experts across industry and academia, influence next-generation platforms, and share insights with the global developer community.

Responsibilities

Design and develop techniques to accelerate high-performance workloads at the intersection of AI, mathematics, and financial systems.
Analyze, optimize, and scale complex AI and HPC workloads for modern CPU and GPU architectures.
Profile and eliminate performance bottlenecks across the stack, from algorithms and kernels to system-level behavior.
Publish and present work in conferences, talks, and blogs to educate and inspire the developer community.
Influence the design of future hardware architectures, system software, libraries, and programming models by collaborating with NVIDIA research, hardware, compiler, and tools teams.

Requirements

Strong hands-on experience with CUDA and parallel programming.
Deep understanding of CPU and GPU architecture fundamentals and their impact on performance.
Master's or PhD in Computer Science, Computer Engineering, Electrical and Computer Engineering, or a related field.
Fluency in C/C++ and a solid foundation in algorithms and software design.
At least 5 years of relevant work or research experience.
Proven experience improving the performance of large-scale computational applications on GPUs.
Excellent understanding of linear algebra.
Strong communication and organizational skills, with a logical approach to problem-solving and solid prioritization abilities.

Preferred Qualifications

Experience with inference optimization techniques and deploying optimized AI models in production.
Experience with TensorRT, TensorRT-LLM, and cuTile.
Experience parallelizing and optimizing machine learning methods such as decision trees, time-series models, and Monte Carlo simulations.

Compensation and Benefits

Base salary range of USD 152,000–241,500 for Level 3.
Base salary range of USD 184,000–287,500 for Level 4.
Salary is determined based on location, experience, and compensation for employees in similar positions.
Eligible for equity and benefits.
Full-time position.