ML Performance Optimization Engineer
About this role
We are looking for the best
AD Divisionμ ML Performance Optimization Engineerλ Autonomous Driving AI λͺ¨λΈμ΄ μ°¨λ νκ²½μμ μμ μ μ΄κ³ ν¨μ¨μ μΌλ‘ λμν μ μλλ‘ λͺ¨λΈ μ΅μ ν λ° μμ€ν μ±λ₯ κ°μ μ 무λ₯Ό μνν©λλ€. Autonomous Driving μ μ¬μ© λλ λ€μν λͺ¨λΈμ λν΄ GPU/NPU κΈ°λ° μ΅μ νλ₯Ό μννλ©°, μ°¨λ νκ²½μμμ λͺ¨λΈ λ°°ν¬ λ° μ±λ₯ ν₯μμ μν ν΅μ¬ μν μ λ΄λΉν©λλ€. λν AI Model λ° Software νκ³Ό κΈ΄λ°ν νμ νμ¬ λͺ¨λΈ μ±λ₯ κ²μ¦, μμ€ν νλ‘νμΌλ§, μ±λ₯ λΆμ μλνλ₯Ό μννλ©° Autonomous Driving μμ€ν μ μμ μ±κ³Ό ν¨μ¨ ν₯μμ κΈ°μ¬ν©λλ€.
The ML Performance Optimization Engineer in the AD Division is responsible for optimizing AI models and improving system performance to ensure autonomous driving models operate efficiently and reliably in vehicle environments. This role focuses on GPU/NPU-based optimization for various autonomous driving models. You will play a key role in enabling seamless deployment and performance improvements on vehicle platforms. The role also involves close collaboration with AI model and software teams to conduct model validation, system profiling, and performance analysis automation for autonomous driving systems.
Responsibilities
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CPU, GPU, Neural Network Accelerator μ±λ₯ λΆμ λ° μ΅μ ν
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Optimize deep learning models using GPU/NPU acceleration
Validate deep learning model performance and conduct system profiling
Analyze and optimize CPU, GPU, and neural network accelerator performance
Develop and automate system performance analysis tools and evaluation metrics
Support deployment and runtime optimization of AI models in vehicle environments
Qualifications
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CPU, GPU, NPU κΈ°λ° Low-level μ±λ₯ μ΅μ ν κ²½ν
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Masterβs degree or higher in Artificial Intelligence, Machine Learning, Deep Learning, or related fields, or equivalent practical experience
Strong understanding of machine learning and deep learning systems
Experience with low-level performance optimization for CPU, GPU, and NPU environments
Proficiency in C/C++, Python, and shell scripting
Development experience in Linux, QNX, or RTOS environments
Experience with system profiling and performance analysis
Preferred Qualifications
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Image Processing, Computer Vision, Robotics Algorithm μ΅μ ν κ²½ν
CUDA, MKL, SIMD, NEON κΈ°λ° μ΅μ ν κ²½ν
NVIDIA κΈ°λ° μ°¨λ νλ«νΌ νκ²½μμμ κ°λ° λλ μ΅μ ν κ²½ν
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Experience optimizing machine learning workloads
Experience optimizing and analyzing linear algebra routines for deep learning systems
Experience optimizing image processing, computer vision, or robotics algorithms
Experience with CUDA, MKL, SIMD, or NEON optimization techniques
Experience developing or optimizing systems on NVIDIA-based vehicle platforms
Experience optimizing AI models for autonomous driving applications
Interview Process
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Resume Screening - Virtual Interview (approximately 1 hour) - Onsite or Virtual Interview (approximately 3 hours) - Final Offer
Please note that the interview process may vary depending on the position and is subject to change based on scheduling and other circumstances.
Interview schedules and results will be communicated individually via the email address provided in your application.
Additional Information
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A 3-month probationary period may apply.
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