Own overall architecture planning and evolution of the company's data centers — spanning compute (GPU/CPU clusters), storage, networking, and infrastructure layers — to support AI training and inference compute demand.

Lead selection, evaluation, and custom design of servers (with a focus on GPU/AI training and inference servers), including complete system solutions and configuration planning for CPU, GPU, memory, storage, and networking components.

Manage end-to-end delivery of data center AI compute projects — from requirement confirmation, resource preparation, and equipment arrival to installation and deployment, integration testing, and final acceptance.

Own supplier quality management for AI compute, covering suppliers of GPU servers, AI accelerators, high-speed networking, storage, key server components, and related infrastructure.

Own full-lifecycle operations of data center resources — racks, rack positions, power, network, servers, and storage — covering demand, allocation, delivery, change, and reclamation.

Own AI Agent product planning and roadmap, define product positioning and target customers, continuously track technical developments in the Agent space, and deliver competitive analysis and product iteration direction.

Own planning and roadmap for Token/MaaS products, define product positioning, target customers, and business models, continuously track technical developments in the field, and deliver competitive analysis and product iteration direction.

Develop the traffic plane and control plane of the LLM inference gateway: multi-provider and multi-model access, routing and scheduling, rate limiting and circuit breaking, retries and failover, key and quota management, and SSE/streaming forwarding — ensuring latency and availability under high concurrency and long-lived connections.

Research and implement core algorithms for LLM inference acceleration — model distillation, PTQ/QAT, structured/unstructured pruning, and sparse inference — systematically reducing inference costs.

Design and build cloud-based inference service platforms for large-scale language and multimodal models, supporting mainstream open-source models (LLaMA, ChatGLM, Mistral series) for online/near-online inference deployment.

Test architecture design and development for LLM inference services — covering functional correctness, performance stability, and user experience reliability, ensuring high-quality delivery of inference APIs and platform.

Design and develop frontend pages for web products, delivering a high-quality user interaction experience.

Server-side architecture design and R&D of core business systems — leading technical reviews and core module coding, ensuring high performance, high availability, and horizontal scalability.

Own test development for the company's AI products and platforms, participate in requirement reviews, and create test strategies and test plans.

Responsible for production service and AI infrastructure stability — container orchestration (Kubernetes), distributed storage (Ceph/MinIO/Redis), high-performance networking (RoCE/InfiniBand) — ensuring 7x24 high availability of core services.

Lead the build-out and continuous improvement of MLPS 2.0, commercial cryptography assessment, ISO 27001, and the information security management system; lead annual assessments and surveillance audits, and liaise with third-party assessors;

Design and implement core data models and service interfaces for tasks, sessions, messages, and execution context.

Design memory data models, storage structures, and access interfaces for different scopes such as personal, project, and workspace.

Build dataset import, cleaning, versioning, quality analysis, and lineage management so that training and evaluation data is traceable.

Own the overall goals of the data engineering team: plan the AI data asset system (full pipeline of collection, cleaning, processing, and evaluation) and deliver high-quality datasets to both the pre-training and post-training consumption lines.

Build and maintain LLM corpus pipelines: crawling, parsing, deduplication, desensitization, and format standardization of text sources such as web pages, books, papers, and code.

Build multimodal data pipelines: collection, decoding, splitting, deduplication, and metadata extraction from image, video, and audio sources.

Develop the data service platform: dataset storage, versioning, lineage tracking, and retrieval services, providing unified data consumption interfaces (API/SDK).

Own the overall goals of the post-training team: plan technical routes such as SFT (Supervised Fine-Tuning), RLHF (Reinforcement Learning from Human Feedback), and DPO (Direct Preference Optimization) to turn base models into deliverable product models.

Research and implement LLM post-training algorithms: SFT data recipes, RLHF/DPO/preference optimization, process reward models (PRM), and other frontier methods.

Research post-training algorithms for multimodal generation models, covering image generation, video generation, and joint audio-video generation; continuously improve generation quality, instruction following, aesthetic quality, temporal consistency, and controllability.

Build the model evaluation system: combining automated benchmarks, human evaluation processes, and online metrics, covering both LLM and multimodal models.

Build and maintain post-training pipelines: SFT/RLHF training frameworks, distributed training jobs, checkpoint-resumed training, and experiment tracking and configuration management.

Develop and sell the company's Token/MaaS services to target customers — AI application developers, internet companies, and customers in finance, government and enterprise, education, and healthcare — independently prospecting and building customer maps and a sales pipeline.

Draft, review, and revise all types of business contracts, including procurement contracts, sales contracts, service agreements, cooperation agreements, and NDAs.

Support the business development team in pre-sales technical engagement, deeply understand customers' AI business scenarios (training/inference/fine-tuning/Agent applications, etc.), and deliver tailored technical solutions and POC designs to drive deal conversion and signing.

Own overall management of the data center AI compute supply chain, covering GPU servers, AI accelerators, high-speed networking, storage, key server components, and related infrastructure resources.

Own procurement of data center AI compute equipment and resources, including GPU servers, AI accelerators, high-speed networking, switches, optical modules, storage, racks, cabling, power, and related infrastructure.

Compute hardware procurement fulfillment: follow through the full order lifecycle for servers, GPU cards, network equipment, and other compute hardware — order confirmation, production schedule tracking, shipment coordination, receiving inspection, warehousing, and reconciliation; read configuration BOMs of mainstream brands and models, and complete receiving inspection and serial number management against acceptance criteria.

As an HR business partner, gain a deep understanding of business strategy and cadence, translate business needs into organization and talent plans, and regularly deliver organizational diagnoses and talent strategy recommendations.

Build the company's recruiting system, including standardized hiring processes, policies, interview assessment tools, and recruiting digitalization (ATS optimization and data dashboards).

Own the company's bookkeeping and overall accounting, with a focus on expense accounting, accounts payable, fixed asset accounting, and R&D expense allocation and supplementary ledgers, ensuring accounts are true, accurate, and compliant.

Own bookkeeping and overall accounting for the overseas company, with a focus on expense accounting, accounts payable, fixed asset accounting, and R&D expense allocation and supplementary ledgers, ensuring accounts are true, accurate, and compliant.