Responsibilities:

1. AI Algorithm Development: Lead the development of original AI algorithms and industry-specific large models for chemistry and materials, building differentiated technical barriers.
2. Technology Productization: Deeply integrate original AI algorithms with practical business scenarios to create efficient and scalable AI products that enhance commercial value.
3. Team Leadership: Build and manage AI teams covering data science, algorithm development, and engineering.

Requirements:

1. Master's degree or above in Computer Science, Data Science, Machine Learning, Computational Chemistry, or related fields.
2. 8+ years of AI R&D and management experience, with successful track record in leading innovative algorithms or large model technology implementation.
3. Experience in building AI teams from scratch and driving technology productization. Priority given to candidates with senior technical management roles in renowned companies.
4. Experience in developing Transformer-based generative models, graph-based deep learning models, or geometric deep learning models.
5. Full-stack capabilities from data preprocessing to model training and deployment.
6. Mastery of mainstream deep learning frameworks (PyTorch, TensorFlow, JAX, Warp) and big data/distributed computing tools.
7. Proficiency in C/C++, CUDA, and Docker development; deep understanding of machine learning engineering and MLOps practices; familiarity with cloud computing platforms and DevOps processes.

Preferred Qualifications:

1. Deep understanding of innovative algorithms and geometric models in AI for Science applications in biology, chemistry, and materials science, or contributions to major codebases in related fields.
2. Rich experience in research, applications, or collaborations in biology, chemistry, or materials science, with ability to accurately understand and drive deep integration of AI technology with scientific domains.
3. Recognition from top academic journals or conferences, with influential publications or achievements in AI for Science.

Responsibilities:

1. Establish laboratory management systems and Standard Operating Procedures (SOPs) to improve operational efficiency and compliance.
2. Oversee facility, high-throughput equipment, and reagent operations and maintenance to optimize resource allocation and ensure efficient operations.
3. Manage laboratory teams, coordinate internal and cross-departmental collaboration, and resolve experimental challenges.
4. Organize technical training and safety education to enhance operational skills and safety awareness.
5. Lead planning, introduction, and deployment of new laboratory facilities and equipment to meet high-throughput R&D and testing needs.
6. Design and implement automated experimental protocols, optimize chemical synthesis, analysis, purification processes, safety management, and risk control.
7. Build comprehensive laboratory safety management systems to ensure compliance with legal regulations and industry standards.
8. Conduct regular laboratory safety inspections, establish emergency response mechanisms, and enhance team capability in handling emergencies.

Requirements:

1. Ph.D. in biochemistry, environmental science, materials science, or related fields; or Master's degree with 5+ years of relevant industry experience.
2. Deep understanding of high-throughput experimentation and materials R&D; background in organic chemistry or medicinal chemistry preferred.
3. Familiarity with high-throughput laboratory automation systems and platforms (e.g., Chemspeed, Syrris).
4. Ability to independently conduct multi-step synthesis and process optimization; expertise in analytical techniques (LC-MS, HPLC, NMR); ability to evaluate compound purity and characteristics.
5. Proficiency in Laboratory Information Management Systems (LIMS) and digital tools, with data analysis and information management capabilities.
6. Excellent laboratory management and cross-departmental communication skills, ability to independently lead teams and drive project implementation.

Responsibilities:

1. Develop computational workflows to support cutting-edge research in chemistry and materials science.
2. Collaborate with cloud and software engineers to design robust data infrastructures
and workflows tailored for chemistry and materials applications.
3. Partner with internal business and technical teams to identify and address emerging
computational needs.
4. Work closely with enterprises and clients to drive industry breakthroughs.

Requirements:

1. Research experience in computational chemistry and materials (e.g., proficiency with
quantum chemistry software like PySCF, Orca). A Master’s degree or higher in materials
science, chemistry, chemical engineering, physics, or a related field is required.
Industry experience is a plus.
2. Strong understanding of chemistry or materials science. Experience with computational
workflows (e.g., FireWorks, AiiDA, Atomate) for catalytic research is preferred.
3. Excellent problem-solving skills, with the ability to independently research
scientific or industrial problems.
4. Proficiency in programming languages such as Python or C++, with strong algorithm
development and implementation capabilities.
5. Interdisciplinary collaboration skills, with the ability to effectively communicate
technical concepts to non-specialists. A growth mindset and commitment to solving
critical scientific discovery challenges through technology.

Preferred Qualifications:

1. Experience building deep learning pipelines, particularly for molecular and materials
design (e.g., GuacaMol, Open Catalyst Project).
2. Hands-on experience with cloud infrastructure and development (e.g., Docker,
Kubernetes, containerization).
3. Startup or entrepreneurial experience is a significant advantage.

Responsibilities:

1. Develop and apply cutting-edge deep learning models to drive innovation in chemistry
and materials science.
2. Design, develop, and maintain machine learning pipelines focused on chemical and
material data, including data collection, preprocessing, feature engineering, model
training, and evaluation.
3. Collaborate closely with cloud and software engineers to design and implement data
infrastructure and workflows centered on chemistry and materials.
4. Work with internal business and technical teams to understand business requirements
and apply machine learning technologies to solve real-world problems, driving commercial
value.
5. Stay at the forefront of machine learning advancements, applying the latest research
outcomes to projects and continuously improving team expertise.

Requirements:

1. Master’s degree or higher in a relevant field (e.g., Chemistry, Computer Science,
Materials Science) with experience in deep learning.
2. Proficiency in at least one deep learning framework (e.g., TensorFlow, PyTorch) with
substantial experience in model development.
3. Strong understanding of machine learning algorithms and theories, particularly in
deep learning areas such as Graph Neural Networks (GNNs), Recurrent Neural Networks
(RNNs), and Convolutional Neural Networks (CNNs).
4. Excellent programming skills in Python or C++, with strong algorithm development and
implementation capabilities.
5. Strong sensitivity to data, with experience in data cleaning, preprocessing, and
feature engineering, as well as extracting valuable insights from data.
6. Passion for AI for Science, with a commitment to advancing chemistry and materials
science through AI technologies.

Preferred Qualifications:

1. Familiarity with chemistry and materials science. Machine learning experience related
to molecules, proteins, or materials is highly valued.
2. Experience with cloud computing platforms (e.g., AWS, Azure, GCP) and
containerization technologies (e.g., Docker, Kubernetes).
3. Experience in developing large-scale software projects, with knowledge of version
control systems (e.g., Git) and code testing processes.
4. Startup or entrepreneurial experience is a strong plus.

Responsibilities:

1. Lead the development and execution of product roadmaps, ensuring alignment with
business objectives and technical feasibility.
2. Conduct market research, analyze user feedback, and stay up-to-date on industry
trends to identify new opportunities.
3. Define product success metrics, track and analyze performance data, and use insights
to inform future product iterations.
4. Bridge the gap between technical teams and non-technical stakeholders by translating
complex concepts into clear, actionable plans. Collaborate with engineering teams to
turn product requirements into technical specifications and development tasks.
5. Prioritize features and manage the product backlog to ensure efficient resource
allocation and timely delivery.

Requirements:

1. 5-7+ years of product management experience, with a preference for candidates from
B2B technology sectors.
2. Proven track record of driving and managing the success of complex technical
products.
3. Strong understanding of AI principles and machine learning algorithms used in
materials or chemistry.
4. Passion for efficient product development and iterative improvement.
5. Exceptional communication, interpersonal, and problem-solving skills.

Preferred Qualifications:

1. Deep understanding of AI and machine learning applications in materials and
chemistry.
2. Startup or entrepreneurial experience is a significant advantage.

Responsibilities:

1. Develop and implement comprehensive business development strategies to support the
company’s overarching goals.
2. Identify, develop, and manage potential clients and partners in industries such as
chemical materials, pharmaceutical intermediates, synthetic biology, and related fields.
3. Build and strengthen relationships with key decision-makers at potential clients and
partners.
4. Oversee negotiation processes to ensure contracts align with business and financial
objectives.
5. Continuously monitor industry trends and competitive dynamics, proactively
identifying new business opportunities and partnerships.

Requirements:

1. 5-10+ years of business development experience, with preference for candidates with
expertise in chemical materials, pharmaceutical intermediates, synthetic biology, AI, or
industrial digitization.
2. Proven experience in developing, maintaining, and managing strategic partnerships.
3. In-depth understanding of B2B sales cycles and the ability to handle complex sales
processes.
4. Exceptional communication, interpersonal, and presentation skills.
5. Self-motivated, results-oriented, and capable of building trust and rapport with
stakeholders.

Preferred Qualifications:

1. Startup or entrepreneurial experience is highly valued.
2. Deep understanding of AI and machine learning applications in biopharma and materials chemistry.