Omni-Modal AI Research Intern

Shine BrightX LLC; Palo Alto (CA); Presencial; USD 80.000 - 125.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Paid sick leave

Paid holidays

Company-sponsored medical plan

Descripción de la vacante

Shine BrightX LLC in Palo Alto, CA is seeking a researcher to advance Omni multimodal large models. You will design training data, develop foundational models, and optimize pre-training, SFT and RL while evaluating capabilities and exploring downstream applications.

You will analyze R&D challenges, identify bottlenecks from first principles, and push leading-edge performance. The role invites research into next‑generation architectures for cross‑modal understanding and generation, with emphasis

Formación

• Bachelor’s degree required; higher degrees preferred.
• Experience with large-scale multimodal data processing.
• Strong foundation in deep learning; diffusion/autoregressive models a plus.
• Publications or cross-modal research experience preferred.
• Experience with distributed training and inference optimization.

Responsabilidades

• Conduct research and development of Omni multimodal large models, including training data design, foundational model algorithm design, optimization of pre-training/SFT/RL, model capability evaluation, and exploration of downstream applications.
• Analyze R&D challenges scientifically, identify performance bottlenecks, and develop solutions based on first principles to accelerate model development and maintain leading-edge performance.
• Explore diverse paradigms for Omni-modal understanding and generation, research next-generation model architectures, and advance multimodal model capabilities.

Conocimientos

Multimodal DL

Data processing

Model development

Distributed training

Diffusion models

ACM/NOI experience

Educación

Bachelor’s degree or higher in CS/AI/math

Herramientas

PyTorch

GPU/CUDA

Descripción del empleo

Shine BrightX LLC in Palo Alto, CA is seeking a researcher to advance Omni multimodal large models. You will design training data, develop foundational models, and optimize pre-training, SFT and RL while evaluating capabilities and exploring downstream applications.

You will analyze R&D challenges, identify bottlenecks from first principles, and push leading-edge performance. The role invites research into next‑generation architectures for cross‑modal understanding and generation, with emphasis