Hunyuan Multimodal Algorithm Researcher Intern(Omni-Modal)

Tencent

Palo Alto (CA)

Sur place

USD 80 168 - 124 800

Plein temps

14 jours+

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Avantages offerts par ce poste

Paid sick leave
Paid holidays
Company-sponsored medical plan

Résumé du poste

Tencent is seeking a dedicated researcher in Palo Alto to conduct innovative R&D on Omni multimodal large models. This role requires a Bachelor's degree in Computer Science or related fields, with a focus on deep learning algorithms and data processing expertise.

The expected salary is competitive, ranging from $80,168.40 to $124,800 per year. Benefits include paid sick leave and a company-sponsored medical plan for eligible interns.

Qualifications

  • Bachelor’s degree (full-time preferred) or higher in Computer Science, Artificial Intelligence, Mathematics, or related fields.
  • Hands-on experience in large-scale multimodal data processing and high-quality data generation.
  • Solid foundation in deep learning algorithms and practical experience in large model development; familiarity with Diffusion Models and Autoregressive Models is advantageous.
  • Proficiency in underlying implementation details of deep learning networks.

Responsabilités

  • Conduct research and development of Omni multimodal large models.
  • Scientifically analyze challenges in R&D and devise solutions to accelerate model development.
  • Explore paradigms for achieving Omni-modal understanding and generation capabilities.

Connaissances

Large-scale multimodal data processing
Deep learning algorithms
Data generation
Strong communication skills
Teamwork

Formation

Bachelor’s degree in Computer Science or related fields

Description du poste

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.
Qualifications
  • 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 & Compensation
  • Location: US-California-Palo Alto.
  • Expected base pay range: $80,168.40 to $124,800.00 per year. Actual pay may vary depending on job‑related knowledge, skills, and experience.
  • Eligibility for 1 hour of paid sick leave for every 30 hours worked and up to 13 paid holidays per calendar year.
  • Full‑time interns eligible to enroll in company‑sponsored medical plan.
Equal Employment Opportunity

As an equal opportunity employer, we firmly believe that diverse voices fuel our innovation and allow us to better serve our users and the community. We foster an environment where every employee of Tencent feels supported and inspired to achieve individual and common goals.

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