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What you’ll be doing:
Cooperate with research teams to onboard new LLMs and VLMs into Nvidia's opensource AI runtimes
Optimize inference workloads using sophisticated profiling and simulation tools
Build SOLID, extendable inference software systems, and refine robust APIs
Implement and debug low-level GPU code to harness the latest HW features
Own end-to-end inference acceleration features and work with teams around the world to deliver production-grade products
What we need to see:
B.Sc., M.Sc. or equivalent experience in Computer Science or Computer Engineering
5+ years of relevant hands-on software engineering experience
Profound knowledge of software design principles
Strong proficiency in at least one system and one scripting language
Strong grasp of machine learning concepts
People person with excellent communication skills that enjoys collaboration and teamwork.
Ways to stand out from the crowd:
Familiarity with Nvidia's DL software stack, e.g. , , and
Proven track record of performance modeling, profiling, debugging, and development in a performance-critical setting with Nvidia's accelerators.
Familiarity with LLM quantization, fine-tunning, and caching algorithms
Proficiency in GPU kernel programming (CUDA or OpenCL)
Prior experience working on a large software project with 50+ contributors
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