GPU Engineer Team Leader
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Monthly Salary:
Experience Required:
Posted on:
7 hours ago
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Vacancies:
1 Vacancy
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Job Summary
About the Opportunity
On behalf of our clients we are hiring a GPU Engineer Team Leader to support their high-performance AI framework engineering team. Our client is an innovative AI infrastructure pioneer specializing in GPU/NPU optimizations and advanced LLM inference frameworks at the intersection of HPC and AI systems.
Joining the team as a GPU Engineer Team Leader you will guide a dedicated team of 4-5 engineers while remaining hands-on with high-performance GPU software development. You will play a pivotal role in driving technical delivery conducting code reviews and optimizing single- and multi-GPU architectures for state-of-the-art AI workloads.
Key Responsibilities
- Team Leadership & Delivery: Lead mentor and develop a small team of GPU/HPC engineers. Plan sprints manage workload distribution and ensure the timely delivery of high-quality software components.
- Mentorship & Culture: Conduct regular 1:1s support engineers career development and foster a culture of continuous improvement and technical excellence.
- Technical Strategy: Translate high-level technical goals from senior management into actionable tasks surface blockers early and maintain clear communication with stakeholders.
- Hands-on Development: Write production-ready low-level GPU kernel code using CUDA HIP or OpenCL for AI training and inference workloads.
- Quality Assurance: Lead code reviews enforce coding standards and perform deep performance profiling and memory hierarchy optimizations to solve critical architectural challenges.
Requirements
Technical skills:
- Bachelors degree in Computer Science Computer Engineering or a related technical field.
- 2 years of professional experience writing system software for GPUs.
- Strong programming proficiency in C and Python.
- Direct experience writing and optimizing GPU software using CUDA HIP or OpenCL.
- Deep knowledge of GPU memory hierarchies including shared memory utilization registers coalescing and occupancy optimization.
- Familiarity with deep learning frameworks (such as PyTorch or TensorFlow) and how they interact with underlying GPU hardware.
Nice-to-have (Preferred Qualifications):
- Experience with distributed GPU computing multi-GPU coordination or parallel runtime systems.
- Strong understanding of AI model architectures (e.g. attention mechanisms matrix operations) and their impact on GPU workload design.
- Hands-on experience with performance profiling tools such as Nsight Compute Nsight Systems or AMD ROCm profiler.
- Active contributions to open-source GPU/HPC projects or publications at top-tier relevant conferences (PPoPP HPDC SC MICRO etc.).
Benefits
- Competitive salary package with performance bonuses.
- Premium healthcare insurance coverage.
- Opportunity to work with cutting-edge HPC multi-GPU systems and generative AI infrastructure.
- Clear career growth paths and continuous professional development support.
- Dynamic open and technical engineering work environment.
Required Skills:
- Bachelors degree in Computer Science Computer Engineering or a related technical field. - 2 years of professional experience writing system software for GPUs. - Strong programming proficiency in C and Python. - Direct experience writing and optimizing GPU software using CUDA HIP or OpenCL. - Deep knowledge of GPU memory hierarchies including shared memory utilization registers coalescing and occupancy optimization. - Familiarity with deep learning frameworks (such as PyTorch or TensorFlow) and how they interact with underlying GPU hardware.
