Staff Deep Learning Engineer

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

Research & Network Design

• 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.
• 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.

Must-Have 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.
• 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

Nice-to-Have 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.

Compensation

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

Benefits

• Health insurance
• HSA
• 401(k) with company match
• Life & disability insurance
• Paid holidays & generous PTO
• Opportunities for bonuses, equity, and career growth