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.
This is our life’s work — to amplify human imagination and intelligence.
AI becomes more and more important in AI-City and self-driving car. NVIDIA is at the forefront of the AI-City and self-driving revolution and providing powerful solutions for them.
All these solutions are based on GPU-accelerated libraries, such as CUDA, cuDNN and TensorRT, etc.
Now, we are now looking for an CPU computing engineer based in Shanghai.
What You’ll Be Doing
- Analysis the TensorRT, CUDA stability and performance issues from customer or internal team
- Work with internationally distributed team with remote locations in US, APAC and India for CUDA and TensorRT developing.
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Extract the feature requirement or FAQ from the analysis and development and generate the documents
- Bachelor of Computer Science or Electrical Engineering is required and Master Degree is preferred.
- 3-5+ years of related work
- Strong programming skills in C and C++ and/or python
- Have knowledge about the popular inference network and layers
- Experience working with deep learning frameworks like PyTorch
- Strong written and verbal communications in both English and Mandarin
- Ability to work well in a diverse team environment as well as with cross site peers
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Strong customer communication skills, powerfully motivated to provide highly responsive support as needed
JR1997003
Senior Embedded Software Engineer
@ Embedd 
What we're looking for
At embedd.it, we power hardware-software integration in embedded devices. We turn fragmented semiconductor chip data into configurable digital twins, then deterministically generate code, tests, and documentation – making integration faster, cheaper, and resilient to supply chain shocks.
We’re looking for a true legend – someone with abstract thinking and deep expertise in low-level software development. You’ll build vendor-agnostic tooling for MCU and peripheral integration to solve hardware-software integration once and for all. We work at the intersection of embedded systems, code generation, compilers, and machine learning. It’s your chance to help redefine how low-level development is done.
Responsibilities
- Drive the architecture of the driver structures and system layout
- Define scalable patterns for cross-architectural integration across bare-metal, Zephyr, Linux, and other environments
- Deconstruct complex SoC device trees and translate hardware descriptions into structured internal models for automated reasoning
- Partner with the ML team to shape data and annotation models for AI-driven data-extraction tooling
Qualifications
- 5+ years of experience in embedded systems development, including bare-metal and RTOS-based systems
- Proficiency in C/C++
- Familiarity with firmware configuration, SoC bring-up, and system-level debugging
- Hands-on experience with code generation tools, or low-level compiler/decompiler/emulator technologies
- Strong background in embedded toolchains: compilers, linkers, debuggers, and build systems
- Python or scripting for automation and tooling is a plus
- Experience with functional-safety standards is a plus
Why embedd.it?
- Dynamic, venture-backed team of engineers challenging the status quo 🚀
- Work at the intersection of embedded systems, code generators, compilers, and ML 🤖
- Unusual challenges requiring out-of-the-box abstract thinking 🧠
- Competitive compensation: cash + equity 💰
- Remote work possible 🌍
About Us
embedd.it is a UK-based startup reinventing embedded development. Our platform transforms unstructured semiconductor data into structured digital twins, enabling automatic generation of drivers, tests, and documentation. We're building a single, traceable source of truth to make integration fast, vendor-agnostic, and future-proof.
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