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.
Currently tracking 109 active AI roles, down 43% versus the prior 4 weeks. Primary focus: Serve · Engineering.
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.
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.
ByteDance is hiring AI talent in: United States (115 roles).
Job postings at ByteDance most frequently mention: Machine Learning, Production ML Systems, Algorithms & Data Structures, GPU Computing, Optimization Methods.
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).
| Title | Stage | AI score |
|---|---|---|
| Research Engineer - LLM Training Infrastructure - Seed Infra Research Engineer focused on large-scale LLM training infrastructure, optimizing distributed training strategies, system reliability, and performance across GPU clusters. The role involves bridging research and production deployment for AI foundation models. | Data | 9 |
| Research Engineer – Reinforcement Learning (RL) Systems & Infrastructure (Seed Infra) Research Engineer focused on building and optimizing distributed reinforcement learning systems and infrastructure for large-scale AI foundation models. This role involves designing end-to-end RL pipelines, optimizing training performance on GPU clusters, and collaborating with researchers on system-algorithm co-design. | DataPost-train |
| 9 |
| Research Engineer – Multimodal Training Infrastructure (Seed Infra) Research Engineer focused on building and optimizing large-scale distributed training infrastructure for foundation models, including multimodal LLMs and image/video generation models. This role involves deep expertise in parallelism strategies, system reliability, and performance optimization on large GPU clusters, bridging research and production deployment. | DataPretrain | 9 |
| Machine Learning Engineer, NLP - TikTok E-commerce Knowledge Graph Machine Learning Engineer focused on NLP and Knowledge Graphs for TikTok E-commerce. Responsibilities include constructing massive product knowledge graphs to enhance feed ranking, recommendations, and ads, and collaborating with cross-functional teams on product strategies. Requires a Bachelor's degree, 3+ years of ML/NLP/CV experience, and proficiency in C++/Python/Go/Java. | Data | 7 |