CUDA Kernel Optimization Specialist - AI Trainer

namename·Remote(United States)
AI & Machine Learning

WFA Digital Insight

As demand for AI and machine learning specialists grows, roles like CUDA Kernel Optimization Specialist are becoming increasingly critical. With a 25% increase in GPU-related job postings in the last year, professionals with expertise in C++, Python, and GPU programming are in high demand. This role stands out for its focus on kernel optimization, a key differentiator in the industry. Before applying, candidates should be aware that a strong understanding of GPU profiler performance metrics and the ability to optimize GPU kernels without deep prior context are essential. With the remote work trend on the rise, this role offers a unique opportunity for professionals to work on complex projects from anywhere

Job Description

About the Role

The CUDA Kernel Optimization Specialist role is a critical part of the AI Trainer team, responsible for analyzing and optimizing GPU kernels for performance, efficiency, and hardware utilization. This role matters because it directly impacts the speed and accuracy of AI models, making it a high-priority area for the company. The ideal candidate will have a strong understanding of GPU profiler performance metrics and be able to optimize GPU kernels without deep prior context. The day-to-day responsibilities of this role will involve working closely with the AI Trainer team to identify bottlenecks and areas for improvement in GPU kernel implementations. The candidate will use profiler metrics to guide kernel improvements and document optimization decisions clearly. This role requires a unique blend of technical expertise and problem-solving skills, making it an exciting opportunity for the right candidate. The company is committed to innovation and excellence in the field of AI and machine learning, and this role is a key part of that effort. The team is composed of experienced professionals who are passionate about their work and dedicated to delivering high-quality results.

What You Will Do

  • Write, modify, and reason about C++17, Python, and GPU programming code to optimize GPU kernels
  • Apply CUDA, HIP, and shader programming expertise to improve performance outcomes
  • Use profiler metrics to guide kernel improvements and document optimization decisions clearly
  • Review GPU kernel implementations to identify bottlenecks and areas for improvement
  • Collaborate with the AI Trainer team to prioritize optimization efforts and ensure alignment with company goals
  • Develop and maintain a deep understanding of GPU architecture and performance characteristics
  • Stay up-to-date with the latest developments in GPU programming and optimization techniques
  • Participate in code reviews and contribute to the improvement of the codebase
  • Develop and maintain a strong understanding of the company's technology stack and how it relates to the role

What We Are Looking For

  • At least 1 year of professional or graduate-level research experience with GPUs
  • Strong understanding of GPU profiler performance metrics for kernel optimization
  • Ability to optimize GPU kernels without deep prior context on every algorithm
  • Fluency in core C++ features through C++17
  • Working knowledge of Python and Git
  • Fluency in at least one GPU programming model like CUDA, HIP, Slang, HLSL, or GLSL
  • Strong problem-solving skills and attention to detail
  • Excellent communication and collaboration skills
  • Ability to work independently and as part of a team

Nice to Have

  • Experience with machine learning or deep learning frameworks
  • Knowledge of computer architecture and parallel processing
  • Familiarity with cloud-based GPU computing platforms
  • Experience with agile development methodologies

Benefits and Perks

  • Competitive compensation package
  • Opportunity to work on cutting-edge AI and machine learning projects
  • Collaborative and dynamic work environment
  • Flexible working hours and remote work options
  • Professional development opportunities and training
  • Access to the latest GPU technology and tools
  • Comprehensive health insurance and benefits package
  • Generous paid time off and holidays

How to Stand Out

  • Be prepared to provide examples of your experience with GPU programming and optimization techniques
  • Make sure your resume and cover letter are tailored to the specific requirements of the role
  • Practice your problem-solving skills and be prepared to answer technical questions during the interview process
  • Consider building a portfolio of your work to showcase your skills and experience
  • Research the company and the role thoroughly to demonstrate your interest and knowledge
  • Be prepared to discuss your experience with Agile development methodologies and collaborative work environments
  • Don't be afraid to ask questions during the interview process to demonstrate your engagement and interest in the role

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