Currently tracking 109 active AI roles, down 43% versus the prior 4 weeks. Primary focus: Serve · Engineering.
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.
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 |
|---|---|---|
| Senior Software Engineer, AI Coding Tools Senior Software Engineer, AI Coding Tools at ByteDance, focusing on the TRAE product, an AI software development agent. The role involves designing, training, fine-tuning, optimizing, quantizing, and deploying LLMs for code generation and reasoning capabilities. It requires experience with large-scale distributed training, model optimization techniques, and deployment on GPU clusters. | Post-trainServe | 9 |
| Sr. Multimodal Model Training and Inference Optimization Engineer Seeking an experienced engineer to optimize large-scale multimodal generative AI model training and inference pipelines, focusing on distributed training strategies and performance bottlenecks for consumer-facing applications like TikTok. | Post-train |
| 9 |
| Sr. Multimodal Model Training and Inference Optimization Engineer Seeking an experienced Multimodal Model Training and Inference Optimization Engineer to optimize AI model training and inference, including distributed training/inference and acceleration, for large-scale generative AI models. Responsibilities include optimizing training pipelines, developing distributed training strategies, and benchmarking/profiling models. | Post-trainServe | 9 |
| Multimodal Model Training and Inference Optimization Engineer Seeking an experienced engineer to optimize large-scale multimodal AI model training and inference pipelines, focusing on distributed training strategies, performance benchmarking, and acceleration for generative AI and CV/Multimodal Understanding applications. | Post-trainServe | 9 |
| Multimodal Model Training and Inference Optimization Engineer ByteDance is looking for an experienced Multimodal Model Training and Inference Optimization Engineer to join their Vision-Applied Research team. This role focuses on optimizing AI model training and inference, including distributed training/inference and acceleration, to enhance the performance, scalability, and deployment of large-scale generative AI models for ByteDance products like TikTok and CapCut. The ideal candidate will have expertise in optimizing AI model training, distributed training strategies, and benchmarking deep learning models. | Post-trainServe | 9 |