AI Engineer (Multimodal AI / RL)

About the Role The AI Engineer will be responsible for designing and building proprietary machine-learning models that reason about electrical and mechanical engineering work for use in automated scoring and evaluation. Responsibilities • Design and build proprietary machine-learning models that reason about electrical and mechanical engineering work (schematics, PCB layouts, CAD geometry, simulations, and design workflows) for use in automated scoring and evaluation • Develop novel model architectures and training pipelines for technical reasoning, not just text generation — including multi-modal reasoning over CAD/ECAD artifacts, simulation outputs, and candidate interaction traces • Translate real engineering tasks (circuit design, debugging, system integration, mechanical design tradeoffs, etc.) into machine-interpretable representations that models can evaluate reliably and deterministically • Build and operate the full learning loop for these models: (a) data generation from real assessment executions (b) trajectory capture (tool use, intermediate designs, decisions) (c) failure analysis (d) targeted dataset curation and “golden” supervision (e) continuous evaluation and model iteration • Create scoring systems that are robust , defensible, and hard to replicate , forming the technical foundation of assessment platform • Work closely with product and platform teams to deploy these models into production scoring pipelines used by real hiring decisions Requirements • Strong background in machine learning, including deep learning and modern foundation model architectures. • Experience designing and operating end-to-end training and evaluation pipelines for production ML systems. • Practical experience with retrieval-augmented generation (RAG) systems and vector databases for large-scale knowledge and artifact retrieval. • Experience working with noisy, real-world labeled datasets, including data cleaning, schema design, and quality control. • Hands-on experience with reinforcement learning , including one or more of: (a) reinforcement learning from human feedback (RLHF) (b) preference modeling and reward model training (c) policy optimization for multi-step or tool-using agents • Experience building or training models on multimodal data (text, images, video, or structured technical artifacts such as diagrams or CAD files)