GPU Engineer Team Leader

• Lead, coach, and develop a team of 4–5 GPU/HPC engineers, promoting a culture of technical excellence, ownership, and continuous improvement.
  
• Plan and coordinate sprint activities, allocate workloads effectively, and ensure the timely delivery of high-quality GPU software components.
  
• Conduct regular one-on-one meetings, provide constructive feedback, and support the professional growth and career development of team members.
  
• Convert high-level technical objectives from senior engineers or engineering management into clear, executable tasks for the team.
  
• Monitor team progress, proactively identify and escalate blockers, and provide clear status updates to relevant stakeholders.
  

Hands-on Technical Contribution

• Develop production-grade GPU kernel code using CUDA, HIP, or OpenCL for AI training and inference workloads.
  
• Lead code review activities and uphold coding standards, GPU optimization practices, and software quality across the team.
  
• Perform performance profiling and optimization of GPU kernels, including memory hierarchy usage and execution efficiency.
  
• Contribute directly to technically complex features and resolve challenging issues that require deep expertise in GPU systems.
  

Minimum Qualifications

• Bachelor’s degree in Computer Science, Computer Engineering, or a related discipline.
  
• Strong proficiency in C++ and Python.
  
• Hands-on experience with CUDA, HIP, or OpenCL.
  
• Experience optimizing GPU memory hierarchies, including shared memory, registers, memory coalescing, and occupancy.
  
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow, including their interaction with GPU computing environments.
  
• Proven experience leading or mentoring a small technical engineering team of at least two members.
  
• Strong analytical and problem-solving capabilities, with the ability to diagnose and resolve complex GPU software issues.
  
• Good written and verbal communication skills to support team coordination, documentation, and stakeholder communication.
  

• Master’s degree or Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence, or a related field.
  
• At least two years of professional experience developing GPU system software.
  
• Experience with distributed GPU computing, multi-GPU coordination, or parallel runtime systems.
  
• Understanding of AI model architectures and their impact on GPU workload design, such as attention mechanisms and matrix operations.
  
• Demonstrated track record of owning end-to-end delivery for a team, module, or technical workstream.
  
• Experience using profiling tools such as Nsight Compute, Nsight Systems, or AMD ROCm Profiler.
  
• Contributions to open-source GPU/HPC projects or publications at relevant conferences such as PPoPP, HPDC, SC, MICRO, or similar venues.