GPU Engineer Lead

Role Overview

We are seeking an experienced GPU Engineer Team Leader to lead a high-performing team of 4–5 engineers focused on developing and optimizing cutting-edge GPU software solutions. This role combines hands-on technical expertise with people leadership, requiring someone who can drive technical excellence, deliver high-quality software, and cultivate a collaborative, growth-oriented team culture.

As a team leader, you will be responsible for guiding day-to-day execution, ensuring successful project delivery, conducting technical reviews, mentoring engineers, and contributing directly to the most complex GPU development and optimization challenges. You will serve as both a key technical contributor and a trusted leader, owning your team's performance, development, and overall success.

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Key Responsibilities

Team Leadership & Delivery

• Lead, coach, and develop a team of 4–5 GPU/HPC engineers, fostering a culture of innovation, accountability, and continuous learning.
• Define sprint priorities, allocate resources effectively, and ensure the timely delivery of high-quality GPU software components.
• Conduct regular one-on-one meetings, provide constructive feedback, and support individual growth and career development.
• Translate strategic objectives and technical direction from engineering leadership into clear, actionable plans for the team.
• Monitor project progress, proactively identify and remove blockers, and communicate status, risks, and milestones to stakeholders.
• Promote engineering best practices, collaboration, and operational excellence across the team.

Hands-on Technical Leadership

• Design, develop, and optimize production-grade GPU kernels using CUDA, HIP, or OpenCL for AI training and inference workloads.
• Drive technical decision-making and lead code reviews to ensure software quality, maintainability, and performance.
• Perform in-depth profiling and optimization of GPU kernels, memory hierarchies, and parallel execution strategies.
• Solve complex performance and scalability challenges by leveraging deep expertise in GPU architectures and systems.
• Collaborate with cross-functional teams to improve overall system performance and accelerate AI workloads.
• Contribute directly to the most challenging technical initiatives, serving as the team's subject matter expert for GPU technologies.

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Minimum Qualifications

• Bachelor's degree in Computer Science, Computer Engineering, or a related discipline.
• Strong programming skills in C++ and Python.
• Hands-on experience with CUDA, HIP, and/or OpenCL.
• Deep understanding of GPU architecture and memory optimization techniques, including shared memory, register utilization, memory coalescing, and occupancy tuning.
• Familiarity with deep learning frameworks such as PyTorch or TensorFlow and their interaction with GPU hardware.
• Proven experience leading, mentoring, or coaching a small engineering team (2+ engineers) in a technical environment.
• Strong analytical and problem-solving abilities with a demonstrated track record of diagnosing and resolving complex GPU-related issues.
• Excellent communication skills, with the ability to coordinate teams and convey technical concepts effectively.

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Preferred Qualifications

• Master's degree or Ph.D. in Computer Science, Computer Engineering, Artificial Intelligence, or a related field.
• 2+ years of professional experience developing low-level or system software for GPU platforms.
• Experience with distributed computing environments, multi-GPU systems, or parallel runtime frameworks.
• Understanding of modern AI model architectures and their implications for GPU workload optimization, including attention mechanisms and large-scale matrix operations.
• Demonstrated ownership of end-to-end delivery for engineering teams, products, or major software modules.
• Experience using GPU profiling and performance analysis tools such as NVIDIA Nsight Compute, Nsight Systems, or AMD ROCm Profiler.
• Contributions to open-source GPU/HPC initiatives, technical publications, or presentations at industry conferences such as PPoPP, HPDC, SC, MICRO, or similar venues.

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What Success Looks Like

• Consistently delivers high-performance GPU software that meets quality, scalability, and performance objectives.
• Builds and develops a motivated, high-performing engineering team.
• Drives continuous optimization of AI and HPC workloads through deep technical expertise.
• Establishes best practices for GPU development, code quality, and engineering excellence.
• Successfully balances technical contribution with effective leadership and team development.

 

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Your Safety and Data Security Matter to Us

Manpower is committed to a safe, secure, and transparent hiring process. We will never request any form of payment, banking details, or sensitive personal information at any stage of recruitment.

If you receive any suspicious communication claiming to be from Manpower, please report it immediately to: [email protected]

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