Chief AI Officer

Responsibilities

1、Build a Unified AI Technology Platform (Internal Core)

• Architecture & Standards: Lead the architecture and roadmap of the integrated LLM + Agent tech stack. Standardize technical specifications and governance frameworks across model, tool, knowledge, and Agent orchestration layers.
• Domain Agent Infrastructure: Oversee the architecture of core R&D Agents (e.g., molecule design, synthetic route planning, automated experimentation, HTE data analytics). Define unified Agent protocols, tool interfaces, and observability standards.
• Data Asset Integration: Connect and harmonize 60+ scientific databases (PubMed, Ensembl, PDB, ChEMBL, etc.) with internal HTE and wet-lab data assets to build end-to-end, auditable, and reproducible AI workflows.
• Shared AI Capability Platform: Drive the development of a shared AI capability platform. Consolidate reusable models, tools, Agents, and knowledge assets to eliminate redundant development across R&D business units.

2、Shape the AI for Science Brand (External Core)

• Brand Storytelling: Partner with the CEO to define the company’s "AI for Science" narrative and core technological value proposition.
• Global Thought Leadership: Represent XtalPi as a keynote speaker or workshop lead at top-tier international conferences (e.g., NeurIPS, ISMB, AI4Science, AAAI).
• Strategic Academic Partnerships: Spearhead strategic academic collaborations with world-class AI labs (e.g., Tsinghua, Peking University, Zhejiang University, Westlake University).
• Industry Impact: Lead 1–2 landmark research papers/patents with industry-wide influence, and champion or co-build milestone AI4S benchmarks.

3、AI Technology Strategy

• Long-Term Roadmap: Define the company’s 3-year AI technology roadmap, lead major architectural decisions, and evaluate emerging global AI trends.
• Cross-Departmental Collaboration: Work in close sync with technical leads across R&D units to provide AI architecture reviews, technical support, and execution plans.

Candidate Profile & Qualifications

Core Profile

We are seeking a top-tier Ph.D. with exceptional potential—a visionary ready to lead AI for Science for the next decade, rather than a conventional legacy manager.

Must-Have

• Top-Tier Ph.D. Background: Ph.D. in AI/CS from world-class institutions or labs (e.g., Tsinghua, Peking University, Zhejiang University, Westlake University, CMU, MIT, Stanford, UC Berkeley, DeepMind, OpenAI).
• LLM Engineering Experience: End-to-end experience leading the training and deployment of large language models (10B+ parameters), or equivalent breakthrough LLM research during doctoral studies.
• Agent Framework Expertise: Hands-on experience with or customized development of mainstream Agent frameworks (e.g., LangChain, LlamaIndex, AutoGen, CrewAI).
• Cross-Disciplinary Communication: Demonstrated ability to communicate effectively with medicinal chemists, biologists, and automation engineers.
• Industry Voice: Proven track record of representing organizations through publications or invited talks at top AI conferences/industry forums.
• Growth Mindset & Long-Term Commitment: Willingness to build architectures from the ground up, with the dedication to thrive in a fast-paced, high-growth environment.

Nice-to-Have

• Direct AI for Science (AI4S) Experience: Background in AI drug discovery, AI materials science, or AI chemistry.
• Top-Tier Publication Record: 3+ publications in top journals/conferences (e.g., NeurIPS, ICML, ICLR, Nature, Science).
• Automated Lab / HTE Familiarity: Understanding of wet-lab workflows, HTE (High-Throughput Experimentation), or automated synthesis.
• Domain Knowledge: Deep familiarity with life science databases (PDB, ChEMBL, UniProt, Ensembl, etc.).
• Industry Experience: 3–5 years of industrial experience (fewer than 10 years is preferred).