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 Scientist - Seed Multimodal Interaction and World Model Research Scientist focused on pioneering AGI through large-scale multimodal foundation models, integrating video, audio, and language with a focus on visual latent reasoning and Reinforcement Learning. The role involves developing unified modeling frameworks and exploring RL-based approaches for multimodal visual reasoning and instruction-conditioned generation, aiming for human-level understanding and interaction capabilities. | PretrainPost-train | 10 |
| Research Scientist - 3D/4D Reconstruction/Generation, PICO Research Scientist focused on 3D/4D reconstruction and generation algorithms and foundation models for MR applications. Responsibilities include developing and optimizing generative models, covering data curation, pre-training, post-training, evaluation, and deployment. | Pretrain |
| 9 |
| Research Scientist, Vision Foundation Model Research Scientist focused on foundational models for visual generation and multimodal generative models. The role involves research and development to enhance strategic advantages for ByteDance products, with a focus on computer vision challenges. Experience with large-scale training and deep learning frameworks is preferred. | PretrainPost-train | 9 |
| Research Scientist - Foundation Model, Speech Understanding Research Scientist focused on foundation models for speech understanding, with a focus on pre-training and fine-tuning. The role involves research and development, collaboration with cross-functional teams, and integration of research findings into practical applications. The team works on multimodal speech technologies, including ASR, speech translation, self-supervised learning, and LLM pre-training/fine-tuning. | PretrainPost-train | 9 |
| Senior Research Scientist, Intelligent Editing (Multimodality) Research Scientist role at ByteDance focusing on multimodal AI for intelligent editing, involving large-scale training and RLHF, with a strong emphasis on computer vision and language understanding. | PretrainPost-train | 9 |
| Research Scientist in Large Language Model (LLM)-Seed Research Scientist role focused on advancing next-generation LLMs, including pre-training, post-training, inference, and interpretability. The role involves exploring large-scale models, optimizing systems, data construction, instruction tuning, preference alignment, and improving model capabilities like reasoning and code generation. | PretrainPost-train | 9 |