Join us at the forefront of AI compiler technology and help shape the future of accelerated computing. NVIDIA is seeking passionate engineers to build the next generation of tools used by AI developers and researchers worldwide. Our team is developing Thunder, an ambitious, source-to-source compiler built to unlock outstanding performance for PyTorch models on NVIDIA GPUs. This is a unique opportunity to contribute to a project that enhances the PyTorch ecosystem, working with modern compiler stacks like PyTorch 2.0's TorchDynamo and TorchInductor to create powerful, open-source solutions that benefit the entire community. If you are driven to solve complex problems and want to make a foundational impact on the AI ecosystem, apply to join our collaborative and innovative team.
What you'll be doing:
As a key member of our team, you will be contributing directly to the future of accelerated AI. Your role will be dynamic and deeply technical, placing you at the center of compiler innovation. You will lead the design, implementation, optimization, and maintenance of the core compiler technologies that accelerate massive deep learning workloads. This is a highly collaborative role where you'll work alongside the very engineers who built PyTorch for NVIDIA hardware, helping to pioneer new features and stay at the forefront of framework development. You'll dive deep into performance analysis, scrutinizing workloads running on thousands of GPUs to find optimization opportunities that will shape the future design of Thunder. Furthermore, you will be part of a vibrant ecosystem, working closely with leading compiler, library, and systems teams—including experts behind nvFuser, TVM, XLA, and CUDA—to translate the latest research into practical, high-impact solutions for the open-source community.
What we need to see:
We are looking for engineers who are excited about building powerful, user-centric tools and are comfortable working in a fast-paced, collaborative environment. Here are some of the expertise we would like to see:
A Bachelor's, Master's, or Ph.D. in Computer Science or a related technical field (or equivalent experience).
8+ years of relevant work experience
A strong command of Python and experience building complex, well-tested software systems.
Hands-on experience with deep learning frameworks like PyTorch or JAX. You understand how models are built and where the performance challenges lie.
A solid foundation in compiler concepts such as abstract syntax trees (ASTs), intermediate representations (e.g., SSA form), program analysis, and code generation.
Excellent communication and collaboration skills, essential for working effectively in a distributed, open-source environment.
Ways to stand out from the crowd:
Previous contributions to deep learning compiler projects (e.g., TVM, MLIR, IREE) or deep learning frameworks themselves.
Deep expertise in the internals of PyTorch, particularly its compiler stack (TorchDynamo, TorchInductor).
Experience with JAX-like functional transformations and their application in a compiler context.
Familiarity with parallel programming, distributed systems, and writing high-performance CUDA code.
A track record of impactful participation in open-source communities, such as through code contributions, design discussions, or mentorship.
NVIDIA is at the forefront of breakthroughs in Artificial Intelligence, High-Performance Computing, and Visualization. Our teams are composed of driven, innovative professionals dedicated to pushing the boundaries of technology. We offer highly competitive salaries, an extensive benefits package, and a work environment that promotes diversity, inclusion, and flexibility. As an equal opportunity employer, we are committed to fostering a supportive and empowering workplace for all.
Your base salary will be determined based on your location, experience, and the pay of employees in similar positions. For Poland: The base salary range is 292,500 PLN - 507,000 PLN.Skills Required
- Bachelor’s, Master’s, or Ph.D. in Computer Science or a related technical field, or equivalent experience
- 8+ years of relevant work experience
- Strong command of Python
- Experience building complex, well-tested software systems
- Hands-on experience with deep learning frameworks such as PyTorch or JAX
- Knowledge of compiler concepts, including abstract syntax trees, intermediate representations, SSA form, program analysis, and code generation
- Excellent communication and collaboration skills
- Contributions to deep learning compiler projects such as TVM, MLIR, or IREE, or to deep learning frameworks
- Deep expertise in PyTorch internals, including TorchDynamo and TorchInductor
- Experience with JAX-like functional transformations in compiler contexts
- Familiarity with parallel programming and distributed systems
- Experience writing high-performance CUDA code
- Impactful participation in open-source communities through contributions, design discussions, or mentorship
NVIDIA Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about NVIDIA and has not been reviewed or approved by NVIDIA.
-
Equity Value & Accessibility — Equity awards and a discounted ESPP are highlighted as core parts of total compensation, enabling employees to share in the company’s success. Stock-based compensation and the two-year lookback ESPP are consistently described as especially valuable.
-
Healthcare Strength — Health coverage is portrayed as robust, with comprehensive medical, dental, and vision options alongside mental health support and on-site care resources. Employer HSA contributions and wellness perks reinforce the depth of the offering.
-
Retirement Support — Retirement programs are depicted as strong, featuring a meaningful 401(k) match with Roth options and support for Mega Backdoor Roth contributions. These elements position long-term savings as a notable advantage of the total rewards package.
NVIDIA Insights
What We Do
NVIDIA’s invention of the GPU in 1999 sparked the growth of the PC gaming market, redefined modern computer graphics, and revolutionized parallel computing. More recently, GPU deep learning ignited modern AI — the next era of computing — with the GPU acting as the brain of computers, robots, and self-driving cars that can perceive and understand the world. Today, NVIDIA is increasingly known as “the AI computing company.”
.jpg)
