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Remote Cuda Developer Jobs in Minnesota (NOW HIRING)

Remote Cuda Developer information

How does a Remote CUDA Developer typically collaborate with team members across different locations?

As a Remote CUDA Developer, you will frequently collaborate with cross-functional teams such as data scientists, software engineers, and product managers through virtual meetings, code reviews, and collaborative platforms like GitHub or GitLab. Clear communication and thorough documentation are essential since team members may be in different time zones. You can expect to participate in regular stand-ups, sprint planning, and peer programming sessions, ensuring alignment and smooth integration of your GPU-accelerated code into larger projects. Tools like Slack, Zoom, and project management platforms help maintain connectivity and workflow efficiency.

What is a Remote CUDA Developer?

A Remote CUDA Developer is a software engineer who specializes in using NVIDIA's CUDA (Compute Unified Device Architecture) platform to develop parallel computing applications, often for high-performance tasks like machine learning, scientific computing, or data analysis. They work remotely, collaborating with teams online rather than being physically present in an office. These developers write and optimize code to run efficiently on NVIDIA GPUs, enabling applications to process large amounts of data much faster than traditional CPU-only solutions.

What are the key skills and qualifications needed to thrive as a Remote CUDA Developer, and why are they important?

To thrive as a Remote CUDA Developer, you need strong proficiency in C/C++ programming, parallel computing concepts, and a solid understanding of GPU architecture, typically backed by a degree in computer science or a related field. Experience with NVIDIA CUDA toolkit, GPU debugging tools, and version control systems like Git is commonly required. Excellent problem-solving skills, self-motivation, and effective remote communication abilities help distinguish high performers in this role. These skills are vital for efficiently delivering high-performance computing solutions and collaborating seamlessly with distributed teams.

What is the difference between Remote Cuda Developer vs Remote Machine Learning Engineer?

AspectRemote Cuda DeveloperRemote Machine Learning Engineer
Required CredentialsCUDA programming certifications, computer science degreeMachine learning certifications, data science background
Work EnvironmentSoftware development, GPU optimizationModel development, data analysis
Industry UsageHigh-performance computing, gaming, AIAI, data science, predictive modeling

Remote Cuda Developers focus on GPU programming and optimization using CUDA, primarily in high-performance computing and AI applications. Remote Machine Learning Engineers develop and deploy machine learning models, often utilizing GPU resources but with a broader focus on data and algorithms. While both roles may involve GPU expertise, Cuda Developers specialize in low-level programming, whereas Machine Learning Engineers work on model development and deployment.

What are the most commonly searched types of Cuda Developer jobs in Minnesota? The most popular types of Cuda Developer jobs in Minnesota are:
What are popular job titles related to Remote Cuda Developer jobs in Minnesota? For Remote Cuda Developer jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Remote Cuda Developer jobs in Minnesota look for? The top searched job categories for Remote Cuda Developer jobs in Minnesota are:
What cities in Minnesota are hiring for Remote Cuda Developer jobs? Cities in Minnesota with the most Remote Cuda Developer job openings:

HPC Customer Solutions Engineer

NextSilicon

Minneapolis, MN • On-site, Remote

Full-time

Posted 17 days ago


Job description

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.

Join our customer success team as an HPC Customer Solutions Engineer. We are seeking a highly skilled, customer-centric individual to act as the primary link between our cutting-edge HPC technology and its end-users.
Operating at the forefront of computer science and scientific applications, you will be instrumental in enabling our customers to achieve peak performance in diverse fields such as graph algorithms, sparse computations, weather prediction, and more. As our platform evolves to incorporate next-generation capabilities (including AI/ML), you will be a key resource in supporting these emerging technologies. You will not only directly support users but actively drive the usability and feature development of our platform by collaborating closely with our product and software engineering teams. If you are passionate about science and technology, thrive in a customer-facing role, and want to shape the future of high-performance computing, we want to hear from you.
We currently have 2 open requisitions for this Customer Solutions Engineer role.
Location: Hybrid in either our Austin, TX or Minneapolis, MN offices preferred but Remote considered for exceptional candidates.
Requirements
  • Master's or higher degree in computer science, engineering, physics, or a related field; candidates may be considered if they have relevant experience in lieu of an advanced degree
  • Proficiency in C, C++, and/or Fortran programming languages
  • Working understanding of standards-based parallel programming models and languages such as MPI, OpenMP, CUDA, or OpenACC
  • Familiarity with HPC software stack components including LLVM compilers and toolchains, libraries, schedulers, and job submission systems (e.g., SLURM or PBS)
  • Experience with Linux/Unix operating systems and command-line tools for system navigation, administration, and troubleshooting
  • Knowledge of performance optimization techniques for parallel computing, including code profiling, tuning, and scaling
  • Exceptional written communication skills with a proven ability to produce clear, concise, and technically accurate documentation
  • Strong verbal communication for effective customer engagement and internal advocacy
  • Results-driven and relentless, with a bias toward creative solutions and a no-excuses approach to delivering outcomes
  • US citizenship with eligibility to visit US government research facilities strongly preferred

Desirable technical skills
  • Familiarity with Python and its relevant data science libraries (e.g., NumPy, Pandas)
  • Familiarity with key AI/ML frameworks (e.g., PyTorch, TensorFlow, or JAX) or working knowledge of optimizing AI/ML workloads on parallel architectures (e.g., GPU acceleration, distributed training)

Responsibilities
  • Provide hands-on support, assisting customers in resolving technical issues for scientific HPC applications
  • Identify, troubleshoot, and reproduce resolving client-facing bugs and runtime errors, ensuring timely resolution and customer satisfaction
  • Drive new software features through direct customer engagement, and resolve client-facing issues.
  • Maintain up-to-date knowledge of key software features and communicate effectively with customers to provide guidance and support
  • Act as a liaison between internal teams, end-users, and relevant stakeholders to advocate for and fulfill customer needs, translating technical requirements into software features
  • Contribute to the development and upkeep of user documentation and support knowledge base
  • Profile and identify bottlenecks in a diverse range of HPC applications across various architectures