CUDA Kernels Engineer
Description For CUDA Kernels Engineer
Join a cutting-edge Silicon Valley startup that's revolutionizing biology and medicine through the power of Generative AI. As a CUDA Kernels Engineer at Genbio, you'll be part of a team pioneering pan-modal Large Biological Models (LBM), working alongside leading innovators in AI and Biological Science.
The role demands expertise in developing and optimizing high-performance GPU/CPU kernels, requiring deep understanding of hardware architectures and CUDA programming. You'll be instrumental in maximizing hardware utilization, developing low-precision algorithms, and collaborating with ML engineers to create efficient model architectures.
The position offers a unique opportunity to work at the intersection of advanced computing and biomedicine, with offices in both Silicon Valley and Paris. You'll be part of a team pushing the boundaries of what's possible in biological modeling and healthcare transformation through AI.
The ideal candidate brings strong coding skills in C/C++ and Python, extensive experience with GPU architectures and CUDA programming, and a background in LLM/foundation model architectures. Whether you're a PhD holder with 1-3 years of experience or have an MS with 3-5 years in the field, you'll find challenging work that directly impacts the future of biological science and medicine.
This role is perfect for someone who finds satisfaction in performance optimization and wants to contribute to groundbreaking advancements in biomedicine. You'll work in an inclusive environment that celebrates diversity and innovation, collaborating with cross-functional teams to develop solutions that could transform healthcare as we know it.
Responsibilities For CUDA Kernels Engineer
- Develop high-performance GPU/CPU kernels and optimize hardware utilization
- Utilize hardware features for aggressive optimizations
- Work with platform teams to deploy kernels and manage training uptime
- Develop low-precision algorithms for high performance with minimal ML accuracy loss
- Collaborate with ML engineers on efficient model architectures
- Work with hardware vendors and advise on HW/SW co-design
Requirements For CUDA Kernels Engineer
- Strong coding skills in C/C++ and Python
- Deep understanding of GPU, CPU, or other AI accelerator architectures
- Experience writing and optimizing compute kernels with CUDA or similar languages
- Familiarity with LLM and foundation model architectures and training infrastructure
- Experience driving ML accuracy with low-precision formats
- 1+ years of relevant industry experience
- PhD with 1-3 years working experience or MS with 3-5 years working experience
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