Graph Neural Network & Reinforcement Learning Engineer Location: Remote/Hybrid Job Type: Part-Time / Full-Time (Remote or Hybrid) Experience Required: 5 to 10 years About the Role : We are seeking a highly experienced Graph Neural Network & Reinforcement Learning Engineer for a cutting-edge PCB design automation platform. The ideal candidate will have hands-on experience training GCNN pipelines and RL agents for complex optimization problems in real-world applications. Key Responsibilities : Design and train Graph Convolutional Neural Network architectures for spatial reasoning tasks Develop and optimize Reinforcement Learning agents for constraint satisfaction problems Build robust data preprocessing pipelines for graph-structured engineering data Implement multi-objective optimization frameworks with competing constraints Validate model performance on real-world design problems and production datasets Collaborate with domain experts to translate engineering requirements into ML objectives Deploy trained models in production environments with performance monitoring Must-Have Skills : 5+ years training GCNN models using PyTorch Geometric, DGL, or similar frameworks Proven experience with RL algorithms : Policy Gradient, Actor-Critic, Q-learning implementations Graph data engineering expertise : adjacency matrices, node/edge features, graph sampling techniques Data curation skills : cleaning, augmentation, and preprocessing of structured engineering data Production ML experience: model versioning, A/B testing, performance monitoring Optimization experience : multi-objective problems, constraint handling, convergence analysis Proficiency in Python, PyTorch/TensorFlow, and distributed training setups Graph-Specific Training Experience : Experience training on large-scale graphs (10K+ nodes) with efficient batching strategies Knowledge of graph convolution variants: GCN, GAT, GraphSAGE Graph augmentation techniques : node dropout, edge perturbation, subgraph sampling Experience with heterogeneous graphs and multi-relational data Understanding of graph pooling and hierarchical graph representations RL Training Experience : Environment design and custom reward function engineering Experience with continuous and discrete action spaces Curriculum learning and progressive difficulty in training Distributed RL training and parallel environment execution Knowledge of exploration strategies and handling sparse rewards Preferred Qualifications : M.Tech/PhD in Computer Science, Machine Learning, or related field Experience with spatial optimization or placement problems Background in constraint satisfaction or combinatorial optimization Prior work on engineering automation What We Offer: Opportunity to apply cutting-edge ML to solve real engineering problems Direct impact on hardware design industry transformation Flexible work arrangements and competitive compensation Equity participation in high-growth startup