포지션 상세
As an AI Research Engineer, you will be at the forefront of innovation, leading the development of next-generation generative AI models and technologies across diverse domains, including LLMs, Text-to-Image, and Text-to-Video. Your work will drive the evolution of AI, blending groundbreaking research with real-world impact, and transforming cutting-edge ideas into transformative solutions that shape the future.
This is more than a job—it’s an invitation to push the boundaries of what’s possible. You’ll immerse yourself in solving some of AI’s most challenging problems, all while creating products that redefine industries and inspire new possibilities. We foster an environment that thrives on curiosity, creativity, and fearless experimentation, empowering you to turn bold ideas into reality.
If you’re passionate about advancing the state of the art in AI, driven to create meaningful impact, and eager to build the future of AI-powered solutions, this role is your opportunity to shine.
Join us, and be part of a team where innovation knows no limits!
• Pre/Mid/Post-training
• Inference
• Scaling Laws
• CoT/Reasoning
• Data processing
• Evaluation
• Alignment
• Architecture
• Distributed training
• LLM/Image/Video/Audio
• MLOps
[You will]
• (Architecture, LLM/Image/Video/Audio) Conduct applied research for advancing neural network architectures of flagship language/image/video/etc models.
• (Evaluation, LLM/Image/Video/Audio, Data processing) Conduct applied research on evaluation criteria for flagship language/image/video/etc models.
• (Pre/Mid/Post-training, Distributed training, Data processing, LLM/Image/Video/Audio) Conduct applied research on large-scale training/efficient sampling/optimization.
• (Pre/Mid/Post-training, Distributed training, Scaling Laws) Conduct applied research for improving the training throughput.
• (Data processing, Evaluation, Pre/Mid/Post-training) Conduct applied research for collecting and evaluating new methods for high quality data.
• (Evaluation, CoT/Reasoning, Pre/Mid/Post-training, LLM/Image/Video/Audio, Alignment, Data processing) Conduct applied research to improve the ability of foundational models to accurately reason about questions of human values, morals, ethics, and cultural norms, and apply these improved models to practical safety challenges.
• (Inference, LLM/Image/Video/Audio) Conduct applied research on generative model architecture to make them more suitable for efficient inference.
• (MLOps, Data processing) Design, build, and operate MLOps systems that can help you efficiently train, infer, and evaluate models.
This is more than a job—it’s an invitation to push the boundaries of what’s possible. You’ll immerse yourself in solving some of AI’s most challenging problems, all while creating products that redefine industries and inspire new possibilities. We foster an environment that thrives on curiosity, creativity, and fearless experimentation, empowering you to turn bold ideas into reality.
If you’re passionate about advancing the state of the art in AI, driven to create meaningful impact, and eager to build the future of AI-powered solutions, this role is your opportunity to shine.
Join us, and be part of a team where innovation knows no limits!
주요업무
[Scope of Responsibilities]• Pre/Mid/Post-training
• Inference
• Scaling Laws
• CoT/Reasoning
• Data processing
• Evaluation
• Alignment
• Architecture
• Distributed training
• LLM/Image/Video/Audio
• MLOps
[You will]
• (Architecture, LLM/Image/Video/Audio) Conduct applied research for advancing neural network architectures of flagship language/image/video/etc models.
• (Evaluation, LLM/Image/Video/Audio, Data processing) Conduct applied research on evaluation criteria for flagship language/image/video/etc models.
• (Pre/Mid/Post-training, Distributed training, Data processing, LLM/Image/Video/Audio) Conduct applied research on large-scale training/efficient sampling/optimization.
• (Pre/Mid/Post-training, Distributed training, Scaling Laws) Conduct applied research for improving the training throughput.
• (Data processing, Evaluation, Pre/Mid/Post-training) Conduct applied research for collecting and evaluating new methods for high quality data.
• (Evaluation, CoT/Reasoning, Pre/Mid/Post-training, LLM/Image/Video/Audio, Alignment, Data processing) Conduct applied research to improve the ability of foundational models to accurately reason about questions of human values, morals, ethics, and cultural norms, and apply these improved models to practical safety challenges.
• (Inference, LLM/Image/Video/Audio) Conduct applied research on generative model architecture to make them more suitable for efficient inference.
• (MLOps, Data processing) Design, build, and operate MLOps systems that can help you efficiently train, infer, and evaluate models.
