AI Algorithm Engineer/年薪 20 万美金内
Responsibilities
• Lead research, pre-training, optimization, and iteration of Vision Foundation Models, including Stable Diffusion, DiT, Vision Transformer (ViT), Segment Anything Model (SAM), CLIP, and other cutting-edge computer vision models.
• Design and fine-tune vision models for business scenarios such as AI image generation, image editing, multimodal understanding, and intelligent visual applications.
• Lead Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (PEFT), LoRA, ControlNet, Adapter tuning, and related optimization techniques.
• Explore and improve generative model architectures and visual feature extraction networks to enhance image quality, stability, resolution, inference efficiency, and multimodal alignment.
• Collaborate with engineering teams to deploy large-scale AI models into production and optimize inference performance through quantization, pruning, inference acceleration, memory optimization, and deployment tuning.
• Continuously track the latest academic research, open-source projects, and industry trends in Computer Vision (CV), Multimodal AI, and Generative AI, rapidly transforming new technologies into business value.
Requirements
Education
• Bachelor's degree or above in Computer Science, Artificial Intelligence, Applied Mathematics, or related fields.
Technical Skills
• Strong expertise in Vision Foundation Models and a deep understanding of Diffusion Models, Vision Transformers (ViT), MAE, and related architectures.
• Experience participating in or leading the complete pre-training lifecycle of large-scale vision or multimodal models with hundreds of millions (or billions) of parameters.
• Strong understanding of dataset construction, data cleaning, training stability, and large-scale model optimization.
• Hands-on experience with AIGC technologies, including Text-to-Image, Image-to-Image, and multimodal generation.
• Proficient in Python and C++, with strong experience using PyTorch and excellent source code reading and secondary development capabilities.
• Familiar with distributed training frameworks such as DeepSpeed, Megatron-LM, and FSDP, with hands-on experience in multi-node, multi-GPU training and compute resource management.
Preferred Qualifications
• Publications in top-tier AI conferences such as CVPR, ICCV, ECCV, NeurIPS, or ICLR.
• Active contributor to GitHub, Hugging Face, or other open-source AI communities.
• High-ranking achievements in AI competitions such as Kaggle.
• Experience with TensorRT, CUDA programming, and low-level operator optimization.
• Experience with High Performance Computing (HPC) environments.
DevOps 年薪 10 万美金内
Core Responsibilities
• Design, build, and maintain AWS cloud infrastructure and private data center environments using Infrastructure as Code (IaC).
• Manage large-scale Kubernetes (EKS) clusters, including cluster deployment, upgrades, scaling, networking (CNI), storage management, and operational maintenance.
• Develop internal DevOps platforms, automation tools, and command-line utilities using Python or Go to improve engineering productivity and operational efficiency.
• Build and maintain end-to-end monitoring and observability platforms based on Prometheus, Grafana, and ELK Stack to ensure system reliability and rapid troubleshooting.
• Manage AI infrastructure, including GPU servers (NVIDIA A100, T4, etc.), CUDA environments, driver versions, and GPU resource allocation.
• Deploy, maintain, and optimize containerized AI applications such as ComfyUI and other Generative AI services for high concurrency and production environments.
Requirements
Education
• Bachelor's degree or above in Computer Science, Information Technology, or related disciplines.
Experience
• Minimum 3 years of experience in DevOps, Site Reliability Engineering (SRE), or Infrastructure Engineering.
Technical Skills
Cloud & Containers
• Strong experience with AWS services, including EC2, EKS, S3, VPC, IAM, and related cloud infrastructure.
• Deep understanding of Kubernetes architecture, scheduling, networking, storage, and container orchestration.
Programming
• Strong programming skills in Python or Go.
• Experience developing backend services, automation tools, or internal DevOps platforms.
System Administration
• Strong knowledge of Linux operating systems.
• Familiarity with TCP/IP, HTTP, DNS, Shell scripting, and system troubleshooting.
CI/CD
• Hands-on experience with Jenkins, GitLab CI/CD, GitHub Actions, or similar continuous integration and deployment platforms.
Preferred Qualifications
GPU Infrastructure
• Experience managing large-scale GPU clusters.
• Knowledge of GPU monitoring, resource scheduling, memory optimization, and Spot Instance cost optimization.
Generative AI Infrastructure
• Hands-on experience deploying and maintaining ComfyUI, Stable Diffusion WebUI, or similar AI inference platforms.
• Experience with dependency management, multi-user concurrency optimization, and AI model loading acceleration.
MLOps
• Familiarity with Kubeflow, MLflow, Triton Inference Server, or similar MLOps platforms.
High Performance Computing
• Experience with RDMA networking, distributed computing, and large-scale parallel processing environments.
TG：hr861
邮箱： [email protected]
Responsibilities
• Lead research, pre-training, optimization, and iteration of Vision Foundation Models, including Stable Diffusion, DiT, Vision Transformer (ViT), Segment Anything Model (SAM), CLIP, and other cutting-edge computer vision models.
• Design and fine-tune vision models for business scenarios such as AI image generation, image editing, multimodal understanding, and intelligent visual applications.
• Lead Supervised Fine-Tuning (SFT), Parameter-Efficient Fine-Tuning (PEFT), LoRA, ControlNet, Adapter tuning, and related optimization techniques.
• Explore and improve generative model architectures and visual feature extraction networks to enhance image quality, stability, resolution, inference efficiency, and multimodal alignment.
• Collaborate with engineering teams to deploy large-scale AI models into production and optimize inference performance through quantization, pruning, inference acceleration, memory optimization, and deployment tuning.
• Continuously track the latest academic research, open-source projects, and industry trends in Computer Vision (CV), Multimodal AI, and Generative AI, rapidly transforming new technologies into business value.
Requirements
Education
• Bachelor's degree or above in Computer Science, Artificial Intelligence, Applied Mathematics, or related fields.
Technical Skills
• Strong expertise in Vision Foundation Models and a deep understanding of Diffusion Models, Vision Transformers (ViT), MAE, and related architectures.
• Experience participating in or leading the complete pre-training lifecycle of large-scale vision or multimodal models with hundreds of millions (or billions) of parameters.
• Strong understanding of dataset construction, data cleaning, training stability, and large-scale model optimization.
• Hands-on experience with AIGC technologies, including Text-to-Image, Image-to-Image, and multimodal generation.
• Proficient in Python and C++, with strong experience using PyTorch and excellent source code reading and secondary development capabilities.
• Familiar with distributed training frameworks such as DeepSpeed, Megatron-LM, and FSDP, with hands-on experience in multi-node, multi-GPU training and compute resource management.
Preferred Qualifications
• Publications in top-tier AI conferences such as CVPR, ICCV, ECCV, NeurIPS, or ICLR.
• Active contributor to GitHub, Hugging Face, or other open-source AI communities.
• High-ranking achievements in AI competitions such as Kaggle.
• Experience with TensorRT, CUDA programming, and low-level operator optimization.
• Experience with High Performance Computing (HPC) environments.
DevOps 年薪 10 万美金内
Core Responsibilities
• Design, build, and maintain AWS cloud infrastructure and private data center environments using Infrastructure as Code (IaC).
• Manage large-scale Kubernetes (EKS) clusters, including cluster deployment, upgrades, scaling, networking (CNI), storage management, and operational maintenance.
• Develop internal DevOps platforms, automation tools, and command-line utilities using Python or Go to improve engineering productivity and operational efficiency.
• Build and maintain end-to-end monitoring and observability platforms based on Prometheus, Grafana, and ELK Stack to ensure system reliability and rapid troubleshooting.
• Manage AI infrastructure, including GPU servers (NVIDIA A100, T4, etc.), CUDA environments, driver versions, and GPU resource allocation.
• Deploy, maintain, and optimize containerized AI applications such as ComfyUI and other Generative AI services for high concurrency and production environments.
Requirements
Education
• Bachelor's degree or above in Computer Science, Information Technology, or related disciplines.
Experience
• Minimum 3 years of experience in DevOps, Site Reliability Engineering (SRE), or Infrastructure Engineering.
Technical Skills
Cloud & Containers
• Strong experience with AWS services, including EC2, EKS, S3, VPC, IAM, and related cloud infrastructure.
• Deep understanding of Kubernetes architecture, scheduling, networking, storage, and container orchestration.
Programming
• Strong programming skills in Python or Go.
• Experience developing backend services, automation tools, or internal DevOps platforms.
System Administration
• Strong knowledge of Linux operating systems.
• Familiarity with TCP/IP, HTTP, DNS, Shell scripting, and system troubleshooting.
CI/CD
• Hands-on experience with Jenkins, GitLab CI/CD, GitHub Actions, or similar continuous integration and deployment platforms.
Preferred Qualifications
GPU Infrastructure
• Experience managing large-scale GPU clusters.
• Knowledge of GPU monitoring, resource scheduling, memory optimization, and Spot Instance cost optimization.
Generative AI Infrastructure
• Hands-on experience deploying and maintaining ComfyUI, Stable Diffusion WebUI, or similar AI inference platforms.
• Experience with dependency management, multi-user concurrency optimization, and AI model loading acceleration.
MLOps
• Familiarity with Kubeflow, MLflow, Triton Inference Server, or similar MLOps platforms.
High Performance Computing
• Experience with RDMA networking, distributed computing, and large-scale parallel processing environments.
TG：hr861
邮箱： [email protected]
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