Applied Scientist - LLM Training & High-Performance Systems

Bytedance; San Jose (CA); On-site; USD 218,000 - 480,000; Full time

Benefits offered by this job

Medical, dental, vision insurance

401(k) with company match

Paid parental leave

Disability coverage

Life insurance

Wellbeing benefits

Holidays and PTO

Job summary

ByteDance seeks a PhD candidate to join AML-MLsys to advance LLM training and inference, RL components, and scalable distributed systems. The role emphasizes building high-performance pipelines across global workloads.

You will collaborate with researchers to push the frontier of LLM/Agent training, optimize GPU/CUDA performance, and contribute to a robust, scalable ML infra used by billions of interactions daily.

Qualifications

• Pursuing a PhD in a technical discipline with strong CS fundamentals.
• Strong knowledge of algorithms and data structures; proficient in Python.
• Understanding of deep learning, neural networks and DL training frameworks (e.g., PyTorch).

Responsibilities

• Develop and optimize LLM training, inference, and RL frameworks.
• Collaborate with model researchers to scale LLM training and RL.
• Optimize GPU/CUDA performance for high‑throughput LLM systems.

Skills

Algorithms and data structures

Python

Education

PhD in computer science / automation / electronics engineering or related

Tools

CUDA

FSDP

Deepspeed

JAX SPMD

Megatron-LM

Verl

TensorRT-LLM

ORCA

VLLM

SGLang

Job description

ByteDance seeks a PhD candidate to join AML-MLsys to advance LLM training and inference, RL components, and scalable distributed systems. The role emphasizes building high-performance pipelines across global workloads.

You will collaborate with researchers to push the frontier of LLM/Agent training, optimize GPU/CUDA performance, and contribute to a robust, scalable ML infra used by billions of interactions daily.