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Cuda Kernel Engineer Jobs in Minnesota (NOW HIRING)

We are looking for an experienced Applications Engineer to join the Applications team, with ... CUDA or similar GPU kernel languages * Hands-on experience with one or more AI/ML frameworks, such ...

We are looking for an experienced Applications Engineer to join the Applications team, with ... CUDA or similar GPU kernel languages * Hands-on experience with one or more AI/ML frameworks, such ...

Cuda Kernel Engineer information

What are common challenges faced by CUDA Kernel Engineers when optimizing GPU code for performance?

Cuda Kernel Engineers often encounter challenges such as managing memory hierarchy efficiently, minimizing data transfer between host and device, and avoiding thread divergence. Ensuring optimal occupancy and maximizing parallelism while preventing bottlenecks like bank conflicts or uncoalesced memory access are also key concerns. Collaborating closely with software architects and data scientists is common, as solutions frequently require balancing algorithmic accuracy with hardware limitations. Addressing these challenges requires continuous profiling, testing, and iterative optimization.

What is a CUDA Kernel Engineer?

Cuda Kernel Engineers are specialized software developers who design, implement, and optimize parallel computing algorithms using NVIDIA's CUDA platform. They write 'kernels,' which are functions that run on Graphics Processing Units (GPUs) to accelerate computational tasks in areas such as machine learning, scientific simulations, and graphics rendering. These engineers need strong skills in C/C++ programming, GPU architecture, and performance optimization techniques. Their work is crucial for applications that require high-speed data processing and efficient resource utilization.

What skills and qualifications are needed to be a CUDA Kernel Engineer?

To thrive as a CUDA Kernel Engineer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid foundation in GPU architectures, typically supported by a degree in computer science or a related field. Expertise in NVIDIA CUDA toolkits, GPU profiling tools like Nsight, and familiarity with version control systems are essential. Analytical thinking, problem-solving abilities, and effective collaboration skills help engineers optimize code and work well within development teams. These skills and qualities are crucial for delivering high-performance, scalable GPU solutions in computationally intensive applications.

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What job categories do people searching Cuda Kernel Engineer jobs in Minnesota look for?

The top searched job categories for Cuda Kernel Engineer jobs in Minnesota are:

What cities in Minnesota are hiring for Cuda Kernel Engineer jobs?

Cities in Minnesota with the most Cuda Kernel Engineer job openings:

HPC Applications Engineer

NextSilicon

Minneapolis, MN • On-site

Full-time

Re-posted 5 days ago


Job description

NextSilicon is reimagining high-performance computing. Our accelerated compute solutions leverage intelligent adaptive algorithms to vastly accelerate supercomputers, driving them forward into a new generation. Our new software-defined hardware architecture enables HPC to fulfill its promise of breakthroughs in all fields of advanced research.

At NextSilicon, everything we do is guided by three core values:

  • Professionalism: We strive for exceptional results through professionalism and unwavering dedication to quality and performance.
  • Unity: Collaboration is key to success. That's why we foster a work environment where every employee can feel valued and heard.
  • Impact: We're passionate about developing technologies that make a meaningful impact on industries, communities, and individuals worldwide.

We are looking for an experienced Applications Engineer to join the Applications team, with demonstrated ability in moving HPC applications onto new platforms and in evaluating performance on node and at scale.

Location: Hybrid in either our Austin, TX or Minneapolis, MN offices (or willing to relocate) preferred but Remote considered for exceptional candidates.

As part of a software-defined hardware company, you will play a pivotal role in driving new software features based on your analysis of customer-defined applications and measuring the resulting performance improvements. This role stands on the edge of computer science/architecture and scientific applications which span a wide range of fields, including but not limited to graph algorithms, sparse computations, weather prediction, seismic imaging, genomics, molecular dynamics, quantum chemistry, and computational fluid dynamics. If you have a passion for science, a knack for solving complex problems, and thrive in a bleeding-edge multidisciplinary environment, we want to hear from you!


Responsibilities:
  • Profile and identify bottlenecks of a wide range of HPC applications on different architectures
  • Develop creative algorithmic and software solutions to solve bottlenecks and accelerate applications on a novel dataflow architecture
  • Translate application requirements into software features and hardware requirements
  • Develop performance models for future hardware architectures

Requirements:
  • US citizenship and eligibility to visit US government research facilities
  • B.S. degree in a hard science, engineering, computer science, or a related field; M.S. or Ph.D. strongly preferred


  • Hands-on experience with development applications in one or more HPC domains, particularly scientific applications that run at rack or system scale
  • High level of proficiency in one of C/C++/Fortran and familiarity with the others
  • Extensive experience with node level and distributed parallel programming models and a working understanding of OpenMP, MPI in particular
  • Ability to measure application-level performance and profile HPC applications at the node level and at scale
  • Willingness to travel as necessary
  • Ability to work remotely and independently in a fast-paced environment with minimal direct supervision


Desired Skills:

  • Expertise in competitive performance analysis is strongly preferred
  • CUDA or similar GPU kernel languages
  • Hands-on experience with one or more AI/ML frameworks, such as pytorch