AI & Synthetic Data Engineer

Role Overview We are looking for an AI & Synthetic Data Engineer with strong mathematical foundations, solid programming logic, and hands-on experience in AI-assisted development and data processing. The ideal candidate should be passionate about synthetic data and capable of applying statistical analysis, machine learning, and other advanced AI techniques to solve practical business and technical problems. Key Responsibilities • Research, design, and implement synthetic data generation and evaluation solutions for different business and technical scenarios. • Process, clean, transform, and analyze structured and unstructured datasets. • Evaluate the quality, representativeness, privacy, and utility of synthetic data using appropriate statistical and machine-learning methods. • Develop prototypes and proof-of-concept solutions using Python and modern AI-assisted coding tools. • Apply machine learning, deep learning, statistical analysis, or reinforcement learning techniques where appropriate. • Collaborate with technical and business stakeholders to translate requirements into practical data and AI solutions. • Document technical approaches, experimental results, assumptions, and recommendations. • Stay current with emerging technologies and best practices in synthetic data, generative AI, privacy-preserving technologies, and data science.

1. 01

   Bachelor degree or above with graduation between September 2026 and August 2027;

2. 02

   Strong logical and computational thinking, with the ability to break down complex problems into structured and executable solutions;

3. 03

   Basic programming skills and experience with AI-assisted development or “vibe coding,” including the effective use of AI coding tools to build, test, debug, and iterate on prototypes; proficiency in Python is strongly preferred;

4. 04

   Strong mathematical foundation, particularly in probability, statistics, linear algebra, optimization, or numerical methods;

5. 05

   Understanding of synthetic data concepts, methodologies, use cases, and evaluation approaches;

6. 06

   Hands-on experience with data-processing and scientific-computing libraries such as Pandas, NumPy, SciPy, Polars, and scikit-learn;

7. 07

   Ability to independently conduct technical research, experiments, and proof-of-concept development, with strong analytical and problem-solving skills;

8. 08

   Practical experience in one or more areas such as machine learning, statistical modeling and analysis, deep learning, reinforcement learning, generative AI, or data privacy and privacy-preserving computation is preferred;

9. 09

   Experience with deep-learning frameworks such as PyTorch, TensorFlow, or JAX is preferred;

10. 10

    Familiarity with synthetic data generation methods, including statistical simulation, generative models, GANs, VAEs, diffusion models, or large language models, is preferred;

11. 11

    Experience evaluating synthetic data across dimensions such as statistical fidelity, downstream utility, bias, privacy risk, and compliance is preferred;

12. 12

    Experience working with large-scale datasets, data pipelines, or cloud-based development environments is preferred;

13. 13

    Strong communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. *Special Reminder: If applying for this position, please ensure that the attached resume includes an English version. Interview Process: The first two rounds of interviews will be conducted online. Final-round candidates will be invited to Beijing for an onsite event, and travel expenses will be reimbursed.