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
Position Overview We are seeking a motivated Electronics Engineer to join our engineering team and contribute to the development and validation of our PCB design automation platform. This role offers the opportunity to work on cutting-edge automation technology while applying hands-on electronics design expertise. Key Responsibilities Design and develop embedded hardware circuits including microcontroller systems, power supplies, and sensor interfaces Create comprehensive schematics, component libraries, and PCB layouts using at least two of these EDA tools: Altium, Cadence, KiCad, and EasyEDA Develop and maintain component footprint libraries and design rule libraries Perform circuit analysis, component selection, and design optimization Test and validate PCB design automation algorithms with real-world design scenarios Develop Python scripts for design verification, component data processing, and automation workflows Required Qualifications Technical Skills Proficiency in at least two of these EDA tools Altium, Cadence, KiCad, and EasyEDA for schematic capture and PCB layout Experience designing embedded hardware systems (microcontrollers, sensors, communication interfaces) Knowledge of power supply design (linear regulators, switching converters, power distribution) Understanding of PCB design principles (signal integrity, EMI/EMC, thermal management) Fluency in Python programming for automation scripting and data processing Familiarity with component selection, datasheet analysis, and BOM management Design Experience Hands-on experience with complete design flow: schematic → PCB → fabrication → assembly → testing Experience with various package types (through-hole, SMD, BGA, QFN) Knowledge of manufacturing constraints and design for manufacturability (DFM) Understanding of common interfaces: I2C, SPI, UART, USB, Ethernet Education & Experience Bachelor's degree in Electronics Engineering, Electrical Engineering, or related field Fresh graduates welcome; 0-3 years of industry experience preferred Minimum 1 complete PCB design cycle from concept to manufactured board Ideal Candidate Profile We're looking for someone who is: Creative and innovative - able to think outside conventional design approaches Detail-oriented - understands that small design decisions have significant impacts Collaborative - comfortable working in a small, dynamic team environment Curious - excited about automation technology and its potential to transform hardware design Practical - has real-world experience turning concepts into working hardware
As an Electronics Engineer at our company, you will be an integral part of our engineering team, contributing to the development and validation of our PCB design automation platform. This role offers you the exciting opportunity to work on cutting-edge automation technology while applying your hands-on electronics design expertise. Your key responsibilities will include designing and developing embedded hardware circuits such as microcontroller systems, power supplies, and sensor interfaces. You will create comprehensive schematics, component libraries, and PCB layouts using EDA tools like Altium, Cadence, KiCad, and EasyEDA. Additionally, you will be responsible for developing and maintaining component footprint libraries and design rule libraries, performing circuit analysis, component selection, and design optimization. You will also test and validate PCB design automation algorithms with real-world design scenarios and develop Python scripts for design verification, component data processing, and automation workflows. In terms of required qualifications, we are looking for candidates with proficiency in at least two EDA tools for schematic capture and PCB layout. You should have experience in designing embedded hardware systems, knowledge of power supply design, and an understanding of PCB design principles. Fluency in Python programming for automation scripting and data processing is essential, along with familiarity with component selection, datasheet analysis, and BOM management. Ideal candidates for this role are creative, innovative, detail-oriented, collaborative, curious, and practical. We welcome fresh graduates with a Bachelor's degree in Electronics Engineering, Electrical Engineering, or a related field, although 0-3 years of industry experience is preferred. The ideal candidate should have completed at least one full PCB design cycle from concept to manufactured board and possess hands-on experience with various package types and knowledge of manufacturing constraints and design for manufacturability. Additionally, familiarity with common interfaces such as I2C, SPI, UART, USB, and Ethernet is beneficial.,