Hunyuan Multimodal Algorithm Researcher (Omni-Modal)
Description
Conduct research and development of Omni multimodal large models, including the design and construction of training data, foundational model algorithm design, optimization related to pre-training/SFT/RL, model capability evaluation, and exploration of downstream application scenarios. Scientifically analyze challenges in R&D, identify bottlenecks in model performance, and devise solutions based on first principles to accelerate model development and iteration, ensuring competitiveness and leading-edge performance. Explore diverse paradigms for achieving Omni-modal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models.
Responsibilities
- Conduct research and development of Omni multimodal large models, including the design and construction of training data, foundational model algorithm design, optimization related to pre-training/SFT/RL, model capability evaluation, and exploration of downstream application scenarios.
- Scientifically analyze challenges in R&D, identify bottlenecks in model performance, and devise solutions based on first principles to accelerate model development and iteration, ensuring competitiveness and leading-edge performance.
- Explore diverse paradigms for achieving Omni-modal understanding and generation capabilities, research next-generation model architectures, and push the boundaries of multimodal models.
Requirements
- Bachelor’s degree (full-time preferred) or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields; graduate degrees are prioritized.
- Hands-on experience in large-scale multimodal data processing and high-quality data generation is highly preferred.
- Solid foundation in deep learning algorithms and practical experience in large model development; familiarity with Diffusion Models and Autoregressive Models is advantageous. Publication in top-tier conferences or experience in cross-modal (e.g., audio-visual) research is preferred.
- Proficiency in underlying implementation details of deep learning networks and operators, model tuning for training/inference, CPU/GPU acceleration, and distributed training/inference optimization; practical experience is a plus.
- Participation in ACM or NOI competitions is highly valued.
- Strong learning agility, communication skills, teamwork, and curiosity.
Benefits
- Employees hired for this position may be eligible for a sign on payment, relocation package, and restricted stock units, which will be evaluated on a case-by-case basis.
- Hired applicants are also eligible for medical, dental, vision, life and disability benefits, and participation in the Company’s 401(k) plan.
- Up to 15 to 25 days of vacation per year (depending on the employee’s tenure).
- Up to 13 days of holidays throughout the calendar year.
- Up to 10 days of paid sick leave per year.
