Job Title

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 andbuild 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.) intomachine-interpretable representationsthat 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 arerobust ,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 decisionsRequirements 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 withreinforcement 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 onmultimodal data(text, images, video, or structured technical artifacts such as diagrams or CAD files)