Total 11 job vacancies by Umilife (singapore) Pte. Ltd.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.
• At least 1 year of relevant expeience.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.
• At least 1 year of relevant expeience.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.
• A minimum of 3 years of relevant professional experience.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Design and optimize large language models, devise fine-tuning strategies, and streamline the training process.
• Explore deep learning architectures like Seq2Seq, Transformer, and advanced techniques including Fine-tuning, Prompt Engineering, and Soft Prompting (SFT).
• Develop systems for efficient model training and deployment, involving data preprocessing, parallel training, and resource management.
• Establish performance evaluation systems and monitor training metrics to ensure model quality and iteration efficiency.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

Descriptions:

• Drive the implementation and refinement of distributed training strategies across multi-GPU and multi-node environments.
• Apply advanced optimization algorithms and their variants (e.g., SGD, Adam, Adagrad) to accelerate learning processes while maintaining model accuracy.
• Lead initiatives in model compression, pruning, and quantization techniques to reduce model footprint and enhance computational efficiency.
• Innovate in knowledge distillation methodologies to transfer learning from larger teacher models to more efficient student models.
• Optimize micro-tuning strategies, such as prompt-based tuning or parameter-efficient tuning methods, to minimize resource requirements.
• Explore and implement mixed precision training and hardware-specific optimizations (e.g., CUDA, Tensor Cores) to leverage hardware acceleration fully.
• Manage hyperparameter tuning processes using automated tools and algorithms to achieve optimal model configurations.
• Collaborate with researchers and engineers to integrate state-of-the-art research into production-ready systems.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.

We are Generative AI team under Monetization Technology. Our team focuses on developing cutting-edge Generative AI techs across all modalities, including text, image, videos, landing pages, etc., and creates industry-leading technical solutions to improve creative efficiency for advertisers, agencies and creators. We are committed to automated creative workflows by leveraging Generative AI technologies, to increase overall revenue for advertisers, agencies and creators.

We aim to drive and lead the generative AI in the ads tech and creative industry, powering products and driving values for our clients, creators, and the whole ecosystem. We are looking for ML engineers who are excited to grow their business understanding, build highly scalable and reliable software/infrastructure, partner across functions with global teams, and make big impacts. If you are someone who welcomes challenges, we are eager to have you on the team!

Descriptions:

• Drive the implementation and refinement of distributed training strategies across multi-GPU and multi-node environments.
• Apply advanced optimization algorithms and their variants (e.g., SGD, Adam, Adagrad) to accelerate learning processes while maintaining model accuracy.
• Lead initiatives in model compression, pruning, and quantization techniques to reduce model footprint and enhance computational efficiency.
• Innovate in knowledge distillation methodologies to transfer learning from larger teacher models to more efficient student models.
• Optimize micro-tuning strategies, such as prompt-based tuning or parameter-efficient tuning methods, to minimize resource requirements.
• Explore and implement mixed precision training and hardware-specific optimizations (e.g., CUDA, Tensor Cores) to leverage hardware acceleration fully.
• Manage hyperparameter tuning processes using automated tools and algorithms to achieve optimal model configurations.
• Collaborate with researchers and engineers to integrate state-of-the-art research into production-ready systems.

Requirements:

• Bachelor degree or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
• LLM. AIGC required.