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 Quidient is hiring a SLAM Engineer to advance the on-device visual-inertial SLAM system that powers our mobile capture experience. This role is central to how everyday users capture the world using their phones.

You will enhance the live tracking pipeline running on mobile, pushing the limits of accuracy, robustness, and real-time AR visualization across a wide range of real-world scenarios — going well beyond what platform-provided solutions can deliver. 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

Real-time SLAM & Sensor Fusion Develop and advance our on-device visual-inertial odometry and VI-SLAM pipeline — including sensor fusion across multi-camera streams, IMU, and depth — with drift correction and relocalization for production capture workflows. Improve robustness across the full range of real-world user behavior — low-light, fast motion, textureless environments, and difficult geometry. Drive down latency and memory footprint on mobile hardware, offloading compute to GPU where possible.

Direct experience with GPU programming (Metal shaders, CUDA kernels, or equivalent) is a strong advantage. Develop and extend calibration pipelines covering camera intrinsics, extrinsics, IMU biases, and time synchronization across multi-sensor mobile configurations. Research Integration & Evaluation Collaborate with the team’s deep learning expertise to evaluate and selectively integrate learning-based methods alongside classical components in the live tracking stack.

Build and extend evaluation methodology grounded in real user captures. Translate research progress into shipped capability in a fast-moving environment.

Qualifications

: Master’s, or PhD in Computer Science, Computer Vision, Robotics, or a related field — or equivalent demonstrated experience. Significant hands-on experience developing or substantially improving a real-time visual-inertial odometry or VI-SLAM system, with a strong emphasis on camera-based pipelines. Strong C++ (modern, performance-aware) and Python (tooling, prototyping, ML work).

Deep working knowledge of multiview geometry, nonlinear estimation, inertial sensing, and the practical failure modes of real-time tracking on resource-constrained devices. Real experience with sensor calibration on real hardware. Ability to read the deep learning literature critically and openness to integrating learned components over time.

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

: Experience with mobile SLAM or VIO on iOS or Android (iPhone strongly preferred). Familiarity with constrained-system development including ARM, threading, memory profiling, and power-aware design. Direct GPU programming experience — Metal shaders, CUDA kernels, or GPU compute APIs — for offloading SLAM or vision workloads on mobile hardware.

Experience with multi-camera and multi-sensor fusion (RGB, depth, IMU) in a mobile or embedded context. Track record of shipping SLAM or visual odometry systems in production on mobile or embedded platforms. Contributor to open-source SLAM or VIO projects (OpenVINS, ORB-SLAM, VINS-Mono, or similar).

Requirements

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

What We Offer

Compensat