Staff Deep Learning Engineer

Quidient is a deep tech AI company pioneering advancements in Generalized (5D) Scene Reconstruction (GSR). GSR is poised to become one of the world’s great digital product categories (think GPS, MRI, and LMM). Our flagship GSR product, Quidient Reality®, is a powerful API that enables anyone with a mobile device to virtualize, visualize, and measure anything.

Words relevant to Quidient include Generative AI, Physics-Informed AI, Large Scene Models (LSMs), Large World Models (LWMs), and API-First. Overview We are seeking a Staff Deep Learning Research Engineer to design, build, and train novel neural network architectures that solve hard problems across Quidient’s GSR platform. This is not an applied-ML role — you will work from foundational principles to create new networks from scratch, implement cutting-edge papers, and run end-to-end experiments across domains including geometric anomaly detection, neural rendering, and 3D reconstruction quality.

This is a hybrid position, meaning that you will need to live within easy driving distance to our Technology Center in Columbia, Maryland.

What You’Ll Do

, dent detection), geometry-based defect identification, and neural rendering improvements. Implement state-of-the-art papers and adapt published architectures to Quidient’s specific reconstruction challenges, exercising deep judgment about what will translate from benchmark to production. Identify technical gaps in the current reconstruction pipeline, propose neural network-based solutions, and build the roadmap for how deep learning capabilities evolve across the platform.

Design and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization. Model Development Run end-to-end experiments independently — from hypothesis through data preparation, training, evaluation, and iteration — with minimal supervision. Stay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.

Contribute production-quality C++ and Python to integrate trained models into the reconstruction engine. Bridge deep learning methods with the geometric and physical foundations of the reconstruction platform, applying domain expertise in one or more of: light transport, 3D reconstruction, or SLAM Drive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.

Qualifications

: Master’s or PhD in Computer Science, Electrical Engineering, Machine Learning, or a related field. A graduate-level foundation in deep learning theory is required, not just applied experience. 6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.

Deep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM. Ability to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems. Strong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.

Willingness to work on-site in Columbia, MD, in a hybrid capacity.

: Experience in fast-paced or startup environments. Publications or open-source contributions in deep learning, neural rendering, 3D reconstruction, or computer vision (CVPR, NeurIPS, ICLR, ICML, SIGGRAPH, or similar).

Requirements

, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification Nice-to-Have