Generative AI Research Engineer — Efficient Models

ByteDance; San Jose (CA); On-site; USD 162,000 - 388,000; Full time

Job summary

ByteDance is seeking a Research Engineer/Scientist to design and implement efficient models for large-scale generative AI, with distillation and compression focus. You will work on transferring capabilities from foundation models into smaller, efficient systems, enabling scalable training, optimization and deployment.

The role emphasizes distillation frameworks, model acceleration, hardware-efficient inference, and collaboration across teams in a fast-paced environment.

Qualifications

• BS in CS or related field or equivalent experience.
• Expertise in efficient models with understanding of bottlenecks and acceleration.
• Experience training generative AI or LLM models using PyTorch and JAX.
• Strong communication and collaboration skills in fast-paced environments.
• PhD in GenAI, MLSys or equivalent.
• Extensive research in GenAI, MLSys, LLM areas.
• Experience in image/video generation, model compression, efficient architectures, or RL training methods.

Responsibilities

• Develop efficient algorithms and architectures for large-scale generative and multimodal models, using distillation, quantization and related methods.
• Advance scalable generative modeling approaches, including diffusion and autoregressive models, focusing on acceleration and efficiency.
• Implement distillation frameworks, model acceleration, and hardware-efficient inference for deployment.
• Collaborate across teams in a fast-paced environment to drive research impact.

Skills

efficient models

large-scale generative AI

PyTorch

JAX

communication skills

Education

B.S. in Computer Science or related fields

Ph.D. in GenAI, MLSys or equivalent

Tools

PyTorch

JAX

Job description

ByteDance is seeking a Research Engineer/Scientist to design and implement efficient models for large-scale generative AI, with distillation and compression focus. You will work on transferring capabilities from foundation models into smaller, efficient systems, enabling scalable training, optimization and deployment.

The role emphasizes distillation frameworks, model acceleration, hardware-efficient inference, and collaboration across teams in a fast-paced environment.