Nvidia is hiring a
Deep Learning Performance Architect
We're now looking for a Deep Learning Performance Architect!
Do you love writing fast code and crafting software systems to solve complex problems? We are looking for hardworking software engineers to help design, build, and ship TensorRT: our SDK for high-performance deep learning inference. Intelligent machines powered by AI computers that can learn, reason and interact with people are no longer science fiction. Today, a self-driving car powered by artificial intelligence can meander through a country road at night and find its way. An AI-powered robot can learn motor skills through trial and error. This is truly an extraordinary time. The era of AI has begun, and we are powering it. If this role seems like a good match for your skills and interests, tell us why you think you might be a great fit for our team, and we'd love to tell you more about what we do!
What you’ll be doing:
Develop production-quality software that ships as part of NVIDIA's AI software stack.
Analyze the performance of important workloads, tune our current software, and propose improvements for future software.
Work with cross-collaborative teams of deep learning software engineers and GPU architects to innovate across applications like autonomous driving, NLP, computer vision, and recommender systems.
Adapt to the constantly evolving AI industry by being agile and excited to contribute across the codebase, including API design, software architecture, testing, and GPU kernel development.
Mentoring junior engineers on the team.
What we need to see:
M.S. Degree in Computer Science, Electrical Engineering (or similar), or equivalent experience.
3+ years of relevant work or research experience.
Strong programming skills in C/C++ development and familiarity with Python.
Experience designing high-level software architecture.
Excellent problem-solving skills, including applications of algorithms and data structures.
Experience with performance analysis, profiling, and code optimization.
Basic understanding of machine learning.
Ability to work independently, define project goals and scope, and lead your own development effort.
Ways to stand out from the crowd:
GPU programming and optimization expertise (e.g. CUDA or OpenCL).
Practical experience with machine learning, especially deep learning.
Experience with data science, statistical analysis, and visualization.
Previous work on large complex codebases with many other developers, especially libraries, compilers, or system software.
Track record of identifying new technologies and incorporating them into software development flows.
Excellent understanding of linear algebra and calculus.
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