ByteDance

2 days ago

Research Engineer – Multimodal Training Infrastructure (Seed Infra)

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Build large-scale multimodal training infrastructure for AI foundation models, focusing on efficient distributed training for multimodal LLMs and image/video generation. Design and optimize parallelism strategies, throughput scaling, and cross-layer performance improvements across GPUs and the training stack. Improve reliability with fast checkpointing and fault tolerance, diagnose failures, and analyze exascale bottlenecks to propose data-driven optimizations for production deployment.

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Education & Training

Key Responsibilities

•Conduct R&D on large-scale infrastructure for efficient training of foundation models, multimodal LLMs, and image/video generation models.
•Design and optimize distributed training strategies, including parallelism schemes and computation/communication optimization for throughput scaling on large GPU clusters.
•Investigate reliability and resilience techniques such as fast checkpointing, fault tolerance, and failure diagnosis for long-running training workloads.
•Research and optimize network, scheduling, and GPU memory management across the training stack to drive cross-layer performance improvements.
•Analyze performance bottlenecks in exascale training systems and translate research ideas into scalable production infrastructure solutions.

Key Requirements

•Deep expertise in large-scale distributed training of LLMs and multimodal models.
•Strong systems research background with ability to design, build, and optimize large-scale ML systems.
•Experience implementing parallelism strategies (data, model, pipeline, expert) and performance optimization on large GPU clusters.
•Strong programming skills with hands-on experience building production-grade ML systems or infrastructure.
•Solid understanding of algorithm–system co-design and cross-layer optimization for training efficiency, scalability, and reliability.

Experience:AIMultimodalDeep learningDistributed trainingReinforcement learning

Tech Stack:Distributed trainingMultimodal LLMsImage/video generationReinforcement learningHigh-performance inferenceGPU clustersParallelismCheckpointingFault toleranceGPU memory managementNetwork optimizationSchedulingHeterogeneous hardware compilationExascale systems

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

2 days ago

Research Engineer – Multimodal Training Infrastructure (Seed Infra)

✓ Verified Job

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

Source: Company careers pageValidated by: Fursa AI

Last checked: 1 hour agoStatus: Live

Job Summary

Location: San Jose

Workplace: Onsite

Employment Type: Full time

Job Function: Education & Training

Key Responsibilities

•Conduct R&D on large-scale infrastructure for efficient training of foundation models, multimodal LLMs, and image/video generation models.
•Design and optimize distributed training strategies, including parallelism schemes and computation/communication optimization for throughput scaling on large GPU clusters.
•Investigate reliability and resilience techniques such as fast checkpointing, fault tolerance, and failure diagnosis for long-running training workloads.
•Research and optimize network, scheduling, and GPU memory management across the training stack to drive cross-layer performance improvements.
•Analyze performance bottlenecks in exascale training systems and translate research ideas into scalable production infrastructure solutions.

Key Requirements

•Deep expertise in large-scale distributed training of LLMs and multimodal models.
•Strong systems research background with ability to design, build, and optimize large-scale ML systems.
•Experience implementing parallelism strategies (data, model, pipeline, expert) and performance optimization on large GPU clusters.
•Strong programming skills with hands-on experience building production-grade ML systems or infrastructure.
•Solid understanding of algorithm–system co-design and cross-layer optimization for training efficiency, scalability, and reliability.

Experience:AIMultimodalDeep learningDistributed trainingReinforcement learning

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