生成式 AI 算法工程师（Generative AI Engineer for Chemistry & Materials）

Job description

我们在建设面向化学与材料研发的生成式 AI 能力，目标是把生成模型与评估/模拟/实验数据协同起来，形成可迭代、可交付的研发工作流。你将负责从数据到模型到决策层的关键模块研发，并与领域专家一起把科学约束变成可计算的模型目标。

工作职责（Responsibilities）

你将围绕“数据—生成—评估—迭代”

的核心链路开展工作

数据与表示：整合公共数据库与内部数据（实验/计算/文献），完成清洗、标准化、去重与数据质量评估，构建可用于训练与评测的高质量数据集。

生成模型研发

设计、训练与优化生成式模型（如 Transformer/GPT、Diffusion、Flow Matching 等），支持面向化学/材料对象的结构生成与优化。

多目标与约束生成

将多种性能目标与物理/化学约束转化为可计算的条件，实现可控生成与多指标权衡。

评测与决策层

建立可靠的评测体系与决策层（打分/过滤/重排序/不确定性），提升结果的有效性、鲁棒性与可复现性。

工程化与协作交付

与领域专家及实验/模拟团队协作，搭建可复现 pipeline（训练/推理/评测/监控），输出可执行候选与阶段性报告，支持闭环迭代。 任职要求（Requirements）“ 硕士及以上学历（博士优先），计算机/人工智能/计算化学/材料科学等相关方向。 扎实的深度学习能力，熟练使用 PyTorch/TensorFlow，能编写整洁、模块化、可维护的研究/工程代码。

具备生成式模型实战经验

至少熟悉并用过 Transformer/GPT、Diffusion、Flow Matching 中的一类（训练、调参、推理、评测）。

良好的数据与系统能力

能独立完成数据管线、实验管理、指标设计与误差分析。

跨学科协作能力强

能把领域约束抽象成算法问题，并推动落地。

加分项（Preferred）

AI for Science 相关落地经验（化学/材料/生物/物理）。 熟悉 RDKit / 分子与材料结构处理工具链 / 图神经网络（GNN）/ 强化学习（RL）。 熟悉分布式训练、加速推理、工业级调参与实验追踪（如 wandb/mlflow/optuna 等）。 有将模型接入真实研发流程（服务化、稳定性、监控、回归测试）的经验 We are building generative AI capabilities for chemistry and materials R&D. Our goal is to integrate generative models with evaluation/simulation/experimental data to form an iterative, deliverable R&D workflow. You will develop key modules across the stack—from data to models to decision-making layers—and work closely with domain experts to turn scientific constraints into computable optimization objectives.

Responsibilities

You will work end-to-end along the core loop: Data → Generation → Evaluation → Iteration: Data & Representations：Integrate public datasets and internal data sources (experiments, computations, literature). Perform cleaning, standardization, deduplication, and data quality assessment. Build high-quality datasets for training and benchmarking.

Generative Model Development

Design, train, and optimize generative model (e.g., Transformers/GPT, Diffusion, Flow Matching). Support structure generation and optimization for chemistry/materials objects.

Multi-objective & Constrained Generation

Translate multiple property targets and physical/chemical constraints into computable conditions. Enable controllable generation and trade-off optimization across multiple metrics.

Evaluation & Decision Layer

Build reliable evaluation and decision modules (scoring, filtering, re-ranking, uncertainty estimation). Improve validity, robustness, and reproducibility of generated results.

Engineering & Cross-functional Delivery

Collaborate with domain experts and experimental/simulation teams. Build reproducible pipelines (training/inference/evaluation/monitoring). Deliver actionable candidates and milestone reports to support closed-loop iteration.

Requirements

M.S. degree or above (Ph.D. preferred) in CS/ML/computational chemistry/materials science or related fields. Strong deep learning fundamentals; proficiency with PyTorch/TensorFlow; ability to write clean, modular, maintainable research/production code. Hands-on experience with at least one class of generative models: Transformer/GPT, Diffusion, or Flow Matching (training, tuning, inference, evaluation).

Strong data/system skills

able to independently build data pipelines, manage experiments, design metrics, and perform error analysis.

Strong cross-disciplinary collaboration skills

able to abstract domain constraints into algorithmic problems and drive implementation.

Preferred Qualifications

· Applied experience in AI for Science (chemistry/materials/biology/physics). Familiarity with RDKit, molecular/materials structure toolchains, GNNs, and/or reinforcement learning. Experience with distributed training, inference acceleration, and industrial-grade tuning/experiment tracking (e.g., Weights & Biases, MLflow, Optuna). Experience integrating models into real R&D workflows (service deployment, reliability, monitoring, regression testing).