Senior GenAI Research Engineer: Efficient Models

ByteDance; Seattle (WA); On-site; USD 242,000 - 456,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Disability insurance

Life insurance

Wellbeing benefits

10 paid holidays per year

10 paid sick days per year

17 days of Paid Personal Time

Job summary

ByteDance’s Vision-Applied Research team seeks a Research Engineer/Scientist to design and implement efficient large-scale generative AI models, with emphasis on distillation and compression. You will work on transferring capabilities from foundation models to smaller, deployment-friendly architectures.

You will develop methods and infrastructure for scalable training, optimization, and deployment, including distillation frameworks, model acceleration, and hardware-efficient inference,

Qualifications

• BS in Computer Science or related field, or equivalent experience.
• Expertise in efficient models with awareness of 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.

Responsibilities

• Develop efficient algorithms and architectures for large-scale generative and multimodal models, using distillation and quantization to improve efficiency.
• Advance scalable generative modeling approaches, including diffusion and autoregressive models, with a focus on acceleration and efficiency.

Skills

Efficient models

PyTorch

JAX

LLM training

Communication

Education

BS in Computer Science or related

PhD GenAI/MLSys (preferred)

Tools

PyTorch

JAX

Job description

ByteDance’s Vision-Applied Research team seeks a Research Engineer/Scientist to design and implement efficient large-scale generative AI models, with emphasis on distillation and compression. You will work on transferring capabilities from foundation models to smaller, deployment-friendly architectures.

You will develop methods and infrastructure for scalable training, optimization, and deployment, including distillation frameworks, model acceleration, and hardware-efficient inference,