Position Overview

We are looking for highly skilled and motivated Machine Learning Engineers/Scientists with a specialized focus on GPU acceleration. In this role, you will be responsible for designing, implementing, and optimizing large machine learning models to leverage GPU capabilities for enhanced performance and scalability. Haiper will equip you with the best computational resources to advance our Perceptual Foundation Models learning endeavors.

Key Responsibilities

• Develop and optimize machine learning models to harness the power of GPUs for accelerated training and inference, ensuring efficient utilization of GPU resources;

• Identify and implement optimization techniques to maximize GPU performance and reduce training time;

• Collaborate with cross-functional teams to scale machine learning models on GPU clusters or distributed GPU environments for large-scale datasets and complex workloads;

• Conduct benchmarking and performance testing to assess the impact of GPU optimizations and ensure model reliability and accuracy;

• Maintain detailed documentation of GPU-related implementations, optimizations, and best practices;

• Collaborate closely with the team in order to trace and deploy the models into production environments;

• Stay informed about advancements in GPU technology and machine learning frameworks to propose and implement innovative solutions.

Requirements

• PhD or Master's in Computer Science, Machine Learning, or a related field;

• Proven experience as a Machine Learning Engineer with a strong emphasis on GPU acceleratio;

• Proficiency in DL frameworks (e.g., PyTorch) and distributed training framework (e.g. Deepspeed);

• Experience in optimizing deep learning models for GPU acceleration;

• In-depth knowledge of GPU architectures and CUDA programing;

• Strong programing skills in languages like Python and C/C++;

• Familiarity with GPU cluster environments and GPU cloud services;

• Excellent problem-solving, communication and analytical abilities.