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Currently tracking 109 active AI roles, down 43% versus the prior 4 weeks. Primary focus: Serve · Engineering.

Hiring
109 / 115
Momentum (4w)
↓-10 -43%
13 opens last 4w · 23 prior 4w
Salary range
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Feb '21
last role today
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ByteDance currently has 112 active AI-related job listings. The majority of these roles are focused on serving infrastructure, accounting for 39% of the total, followed by agents at 27%. Engineering is the most frequent function, with research also being a significant area. The company is hiring for these positions primarily in the United States. Frequent tech tags include model_serving, inference_infra, and multimodal. In the last 30 days, ByteDance added 14 new AI roles, representing a 27% increase compared to the previous 30-day period.

Auto-generated from active job postings · last refreshed 2026-05-24

Frequently asked questions

  • What AI roles is ByteDance hiring for?

    ByteDance currently has 115 active AI-related roles in our index. The most common open titles are: Cloud Acceleration Engineer – DPU & AI Infra (2), LLM AIOps Development Engineer - Data Center Networking (2), Multimodal Model Training and Inference Optimization Engineer (2), Research Engineer - LLM Training Infrastructure - Seed Infra (2), Research Engineer - LLM/VLM Inference Optimization (Seed Infra) (2). Most positions are in Engineering and Research.

  • What stage of AI development does ByteDance focus on?

    ByteDance's active AI hiring is concentrated in: serving infrastructure (38%), agents (25%), post-training (12%). These categories follow a seven-stage AI lifecycle: data, pre-training, post-training, serving infrastructure, agents, evaluation, and application.

  • Where is ByteDance hiring AI talent?

    ByteDance is hiring AI talent in: United States (115 roles).

  • What skills does ByteDance look for in AI roles?

    Job postings at ByteDance most frequently mention: Machine Learning, Production ML Systems, Algorithms & Data Structures, GPU Computing, Optimization Methods.

  • How many AI roles has ByteDance posted recently?

    In the past 30 days, ByteDance has posted 2 new AI-related roles. That is a -78% change versus the prior 30 days (9 → 2).

Jobs (10)

109 AI · 270 total active
FilteredStageServe×FunctionResearch×Clear all
Show
Active onlyAI only (≥ 7)
Stage
AllData · 11Pretrain · 8Post-train · 16Serve · 41Agent · 29Eval Gate · 2Ship · 8
Function
AllEngineering · 74Research · 40Product · 1
Sort
AI scoreRecentTitle
TitleStageFunctionLocationFirst seenAI score
Senior Research Scientist/Engineer - AI Infrastructure
Seeking an experienced Research Scientist/Engineer to design and build next-generation AI infrastructure at ByteDance, focusing on large-scale systems, AI, and emerging hardware to enable efficient and scalable AI workloads. The role involves architecting the end-to-end AI factory, exploring emerging trends, optimizing ML stack performance, and aligning cross-functional teams.
ServeDataResearchSan Jose, CAJan 309
Tech Lead, Research Scientist/Engineer - AI Infrastructure
Research Scientist/Engineer role focused on defining and building next-generation AI infrastructure for large-scale AI workloads, including training, RL, and inference, considering compute, storage, networking, chips, power, and data layers. The role involves tracking AI trends, optimizing system performance, and aligning cross-functional teams.
ServeData
Research
San Jose, CA
May '25
9
Research Engineer / Scientist - Storage for LLM
Research Engineer/Scientist focused on designing and implementing a high-performance KV cache layer for LLM inference to improve latency, throughput, and cost-efficiency in transformer-based model serving.
ServeResearchSan Jose, CAMay '258
Senior Research Engineer / Scientist -AI for Databases
Research Engineer/Scientist focused on applying AI/ML to database management systems, including query optimization, indexing, workload forecasting, and developing self-managing databases. The role involves integrating AI models into production systems and publishing research findings.
ServeDataResearchSeattle, WAMay '258
Research Engineer / Scientist -AI for Databases
Research Engineer/Scientist role focusing on applying AI/ML to database management systems, including query optimization, indexing, workload forecasting, and developing self-managing databases. The role involves research and development, integrating AI models into production systems, analyzing large datasets, and publishing findings. Requires a PhD and strong publication record in AI/databases/systems, with experience in database internals and ML frameworks.
ServeDataResearchSeattle, WAMay '258
Research Engineer / Scientist -AI for Databases
Research Engineer/Scientist focused on applying AI/ML to database management systems, including query optimization, indexing, and workload forecasting, with a goal of building AI-native data infrastructure and intelligent optimization. The role involves research and development, integrating models into production, and publishing findings.
ServeDataResearchSan Jose, CAMay '258
Research Scientist - DPU & AI Infra
Research Scientist focused on DPU and AI infrastructure, aiming to accelerate distributed training and inference by co-designing software and hardware solutions. Explores AI/ML infrastructure acceleration leveraging DPUs, GPUs, and custom hardware.
ServeDataResearchSan Jose, CASep '257
Senior Research Scientist - DPU & AI Infra
Research Scientist role focused on designing and developing DPU network software for AI/ML workloads, optimizing distributed training and inference, and exploring software-hardware co-design for cloud and AI computing infrastructure.
ServeDataResearchSeattle, WASep '257
Research Scientist - DPU & AI Infra
Research Scientist role focused on designing and developing DPU network software for AI/ML workloads, including distributed training and inference acceleration, and software-hardware co-design.
ServeDataResearchSeattle, WASep '257
Tech Lead, Research Scientist - DPU & AI Infra
This role focuses on designing and developing DPU network software and exploring AI/ML infrastructure acceleration using DPUs, GPUs, and custom hardware to optimize distributed training and inference. It involves software-hardware co-design and end-to-end performance optimization for cloud-scale computing.
ServeDataResearchSan Jose, CASep '257