长江存储Module Diffusion Equipment Engineer/Technical Manager(J11281)

"1. 负责个人及机台操作安全之维护 2. 藉由工艺改善提升生产力 3. 工艺参数及操作规范的建立及维护. 4. 生产线稳定性的维护, 5. 原料预算编列及后续执行控管. 6. 建立原料成本降低计划及第二供货商验证导入. 7. 新技术导入及大量生产时程控制. 8. 新型机台验证导入之计划与执行"

面向AIGC领域，研发前沿的视频生成与处理算法，结合短视频、电商、品牌创意等具体业务场景，进行系统性算法设计，推动自动化剪辑、视频生成、动作迁移、语义驱动等能力落地； 针对当前大模型视频生成中的痛点（如帧一致性、时空建模、长视频连贯性、跨模态对齐等），优化扩散/生成架构、设计稀疏高效推理策略，提高生成质量和响应速度； 开发用于视频创作的底层算法与工具链，包括视频分镜生成、关键帧补全、文本驱动编辑（text-driven editing）、镜头分割与结构化剪辑等能力模块； 持续追踪业界前沿（如Sora、Runway、Kling、Veo等），快速完成benchmark与迁移落地； 深度理解视频内容生产到多渠道分发的完整链路，与产品、运营、创意团队协同，构建适配业务的AI视频引擎与应用原型。

Location & Duration Sydney Central; 6-12 months Role Overview You will participate in the research and development of human aesthetic enhancement and spatiotemporally consistent editing technologies at Meitu. You will work directly with real, product-scale datasets and state-of-the-art algorithms. Depending on the internship track, your work may include (but is not limited to): ·      Fine-grained and controllable image / video aesthetic enhancement ·      2D / 3D human tracking and 3D reconstruction ·      Regression, reconstruction, and structural constraints of digital human models (e.g., SMPL) This role offers the opportunity to produce both production-ready technical outcomes and high-quality academic research results. It is a research-and-engineering-oriented internship, ideal for candidates with strong interest and capability in 3D vision fundamentals, human visual quality enhancement, video generation models, and 3D human modelling. Key Responsibilities ·      Research and implement algorithms related to depth estimation, multi-view generation, and 2D / 3D tracking with spatiotemporal reconstruction ·      Follow state-of-the-art 3D vision papers and open-source projects; reproduce experiments and adapt methods to practical applications ·      Collaborate with data teams to refine the 3D aesthetic development pipeline, improve data collection and quality evaluation, and establish foundations for high-quality scaling ·      Explore the integration of human structure priors (Skeleton / SMPL / Mesh) with multi-modal cues such as depth, normals, and optical flow in reconstruction and generative models ·      Assist in building data processing, evaluation, and visualization tools (e.g., immersive video aesthetic editing) to support rapid iteration ·      Enable high-quality projection of 3D features into 2D visual outputs, with the goal of producing A-level or above academic publications

About the Team We are a visual R&D team focused on human aesthetic modelling and advanced 3D vision research. Our work spans human image understanding, 3D reconstruction, and intelligent aesthetic enhancement. By combining academic research methodologies with real-world product deployment, we continuously explore new frontiers in AI-driven image/video generation, editing, spatiotemporal consistency, and 3D structural understanding. Location & Duration Sydney Central; 6-12 months Role Overview You will participate in the research and development of human aesthetic enhancement and spatiotemporally consistent editing technologies at Meitu. You will work directly with real, product-scale datasets and state-of-the-art algorithms. Depending on the internship track, your work may include (but is not limited to): ·      Fine-grained and controllable image / video aesthetic enhancement ·      2D / 3D human tracking and 3D reconstruction ·      Regression, reconstruction, and structural constraints of digital human models (e.g., SMPL) This role offers the opportunity to produce both production-ready technical outcomes and high-quality academic research results. It is a research-and-engineering-oriented internship, ideal for candidates with strong interest and capability in 3D vision fundamentals, human visual quality enhancement, video generation models, and 3D human modelling. Key Responsibilities ·      Research and implement algorithms related to depth estimation, multi-view generation, and 2D / 3D tracking with spatiotemporal reconstruction ·      Follow state-of-the-art 3D vision papers and open-source projects; reproduce experiments and adapt methods to practical applications ·      Collaborate with data teams to refine the 3D aesthetic development pipeline, improve data collection and quality evaluation, and establish foundations for high-quality scaling ·      Explore the integration of human structure priors (Skeleton / SMPL / Mesh) with multi-modal cues such as depth, normals, and optical flow in reconstruction and generative models ·      Assist in building data processing, evaluation, and visualization tools (e.g., immersive video aesthetic editing) to support rapid iteration ·      Enable high-quality projection of 3D features into 2D visual outputs, with the goal of producing A-level or above academic publications