Staff Deep Learning Engineer

Role overview

Responsibilities

• check_circle Design and train novel deep neural network architectures from scratch for a variety of reconstruction tasks — including surface anomaly detection (e.g., dent detection), geometry-based defect identification, and neural rendering improvements.
• check_circle 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.
• check_circle 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.
• check_circle Design and maintain rigorous evaluation pipelines grounded in real-world captures to measure model performance, regression, and generalization.
• check_circle Run end-to-end experiments independently — from hypothesis through data preparation, training, evaluation, and iteration — with minimal supervision.
• check_circle Stay current with the latest advances in deep neural network architectures, training techniques, and optimization methods, continuously bringing relevant ideas into the pipeline.
• check_circle Contribute production-quality C++ and Python to integrate trained models into the reconstruction engine.
• check_circle 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
• check_circle Drive inference optimization and GPU/CUDA performance work toward real-time and on-device targets.
• check_circle 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.
• check_circle 6+ years of experience in deep learning research and engineering, with demonstrated ability to design, train, and evaluate novel neural network architectures from scratch.
• check_circle Deep domain expertise in at least one of: light transport, deep learning for 3D vision, or SLAM.
• check_circle Ability to read, critically evaluate, and implement current deep learning papers (CVPR, NeurIPS, ICLR, ICML) and translate them into working systems.
• check_circle Strong software engineering in C++ and Python, with deep proficiency in PyTorch or equivalent frameworks for model development and training.
• check_circle Willingness to work on-site in Columbia, MD, in a hybrid capacity.
• check_circle Meet Quidient, customer, and government security requirements, which may include, but are not limited to a background check, citizenship verification, and Criminal Justice Information Services verification

Preferred qualifications

• 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).
• Experience designing evaluation pipelines and experiment infrastructure for deep learning research.
• Hands on with geometric or physics-informed neural networks, or anomaly detection in 3D data.
• Track record of taking a research idea from paper to production-deployed model.

Benefits

• check_circle Salary Range: $185,000 – $235,000.
• check_circle Annual bonus and equity as appropriate.