Research Engineer/Scientist (all levels), Efficient Models San Jose Regular

ByteDance; San Jose (CA); On-site; USD 156,000 - 387,600; Full time

Job summary

ByteDance in San Jose is hiring a Research Engineer/Scientist to design and implement efficient models for large-scale generative AI. This role emphasizes collaboration and requires expertise in deep learning and generative models.

The ideal candidate should have a B.S. in Computer Science and proficiency in frameworks like PyTorch and JAX. The position offers a base salary range of $156,000 – $387,600 annually.

Qualifications

• B.S. in Computer Science or equivalent experience required.
• Expertise in efficient models and deep understanding of bottlenecks.
• Proficiency in training generative AI models using PyTorch and JAX.

Responsibilities

• Design and implement efficient models for generative AI.
• Develop algorithms and architectures for generative and multimodal models.
• Advance scalable generative modeling approaches with focus on efficiency.

Skills

Efficient models

Deep learning

PyTorch

JAX

Communication skills

Collaboration skills

Education

B.S. in Computer Science or related field

Ph.D. in GenAI, MLSys or equivalent experience

Tools

Large-scale generative AI

Model distillation

Model compression

Job description

Research Engineer/Scientist (all levels), Efficient Models

Location: San Jose

Responsibilities

• Take initiatives in designing and implementing efficient models for large‑scale generative AI, with emphasis on large model distillation and compression.
• Develop efficient algorithms and architectures for large‑scale generative and multimodal models using techniques such as step distillation, cfg distillation, quantization, and other methods.
• Advance scalable generative modeling approaches, including diffusion and autoregressive models, with focus on acceleration and efficiency.
• Contribute to distillation frameworks, model acceleration, hardware‑efficient inference, and their applications.
• Develop methods and infrastructure for transferring capabilities from foundation models into smaller, more efficient models.

Minimum Qualifications

• B.S. in Computer Science or related field, or equivalent experience.
• Expertise in efficient models with deep understanding of computational bottlenecks and acceleration methods.
• Proficiency in training generative AI or LLM models using PyTorch and JAX.
• Strong communication and collaboration skills in fast‑paced environments.

Preferred Qualifications

• Ph.D. in GenAI, MLSys or equivalent experience.
• Extensive research experience in broad GenAI, MLSys, LLM areas.
• Proven experience in at least one of: image/video generation and editing; model compression (quantization, step/cfg distillation); efficient architectures (MoE, window attention); efficient model design; or reinforcement learning training methods (RLHF, DPO, GRPO).

Job Information

Base salary range: $156,000 – $387,600 annually.