ByteDance

1 day ago

Inference Systems Backend Engineer - ARK Large Model Platform (Singapore)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Build and optimize Volcano Engine’s large model training and inference systems, including computation optimization, tuning thousand-GPU clusters, distributed LLM inference, and large-scale traffic scheduling. Tackle high-concurrency reliability and scalability challenges for workloads measured in hundreds of billions of tokens. Advance training/inference architectures (e.g., subgraph matching, compiler optimization, quantization), integrate heterogeneous GPU/NPU/TPU hardware, and improve utilization via elastic scheduling and GPU oversubscription while partnering with algorithm teams.

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop and performance-optimize Volcano Engine large model training and inference systems, including computation optimization, thousand-GPU cluster tuning, distributed LLM inference, and large-scale inference traffic scheduling.
•Solve technical challenges for high concurrency, high reliability, and high scalability to support daily training and inference traffic at hundreds of billions of tokens.
•Research and introduce forward-looking training and inference architectures such as subgraph matching, compiler optimization, and model quantization.
•Integrate heterogeneous hardware with training and inference frameworks across GPUs, NPUs, and TPUs.
•Improve compute utilization across globally distributed ultra-large-scale GPU clusters using elastic scheduling, GPU oversubscription, and task orchestration; collaborate with algorithm teams to optimize algorithms and systems.

Key Requirements

•Proficiency in C/C++ and Python development under Linux environments, with experience in large-scale ML systems or search, advertising, and recommendation systems.
•Familiarity with at least one machine learning framework such as TensorFlow, PyTorch, or MXNet (or similar in-house frameworks).
•Familiarity with at least one large model training or inference framework, including vLLM, TensorRT-LLM, SGLang, or Megatron-LM.
•Strong problem-solving ability and capability to work independently while collaborating effectively as a team.
•Strong sense of responsibility, good learning ability, communication skills, and self-motivation.

Experience:Large-scale ML

Education:

Skills:Problem-solvingTeamworkCommunicationSelf-motivation

Tech Stack:CC++PythonLinuxTensorFlowPyTorchMXNetVLLMTensorRT-LLMSGLangMegatron-LMCUDACuDNNVolcanoEngineGPUNPUTPU

Company Brief

ByteDance

Develops consumer internet and content platforms, including TikTok and other apps for short-form video, news, and entertainment. It also builds advertising, commerce, and creator tools that connect audiences, brands, and publishers across global markets.

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn

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ByteDance

1 day ago

Inference Systems Backend Engineer - ARK Large Model Platform (Singapore)

✓ Verified Job

Canonical indexed version, validated from employer's careers page.

Source: Company careers pageValidated by: Fursa AI

Last checked: 23 hours agoStatus: Live

Job Summary

Location: Singapore

Workplace: Onsite

Employment Type: Full time

Job Function: Software Engineering

Key Responsibilities

•Develop and performance-optimize Volcano Engine large model training and inference systems, including computation optimization, thousand-GPU cluster tuning, distributed LLM inference, and large-scale inference traffic scheduling.
•Solve technical challenges for high concurrency, high reliability, and high scalability to support daily training and inference traffic at hundreds of billions of tokens.
•Research and introduce forward-looking training and inference architectures such as subgraph matching, compiler optimization, and model quantization.
•Integrate heterogeneous hardware with training and inference frameworks across GPUs, NPUs, and TPUs.
•Improve compute utilization across globally distributed ultra-large-scale GPU clusters using elastic scheduling, GPU oversubscription, and task orchestration; collaborate with algorithm teams to optimize algorithms and systems.

Key Requirements

•Proficiency in C/C++ and Python development under Linux environments, with experience in large-scale ML systems or search, advertising, and recommendation systems.
•Familiarity with at least one machine learning framework such as TensorFlow, PyTorch, or MXNet (or similar in-house frameworks).
•Familiarity with at least one large model training or inference framework, including vLLM, TensorRT-LLM, SGLang, or Megatron-LM.
•Strong problem-solving ability and capability to work independently while collaborating effectively as a team.
•Strong sense of responsibility, good learning ability, communication skills, and self-motivation.

Experience:Large-scale ML

Education:

Skills:Problem-solvingTeamworkCommunicationSelf-motivation

Tech Stack:CC++PythonLinuxTensorFlowPyTorchMXNetVLLMTensorRT-LLMSGLangMegatron-LMCUDACuDNNVolcanoEngineGPUNPUTPU

Company Brief

ByteDance

Industry: Digital Media

Company Size: Enterprise (1,001+ employees)

Revenue: USD 1B+

Growth: Established Company

Headquarters: Beijing, China

Founded: 2012

Website LinkedIn