GPU Engineer Team Leader

• Lead coach and develop a team of 45 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.
  

Requirements

Minimum Qualifications

• Bachelors 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.
  

• Masters 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.