Machine Learning Engineer

Job Duties: Lead and manage the deployment and management of machine learning models across production systems. Collaborate with machine learning engineers and data scientists to design and implement efficient and scalable machine learning pipelines, monitoring, and testing frameworks, and data management processes. Work with software engineers to ensure the seamless integration of machine learning models into applications and services. Develop and maintain efficient and scalable data management processes for large-scale machine learning datasets. Collaborate with the Infrastructure team to ensure the reliability, scalability, and security of production systems. Stay up-to-date with the latest trends and best practices in MLOps, machine learning, and data management. Advocate for tools and technologies to accelerate research. Distribute neural networking training across fleets of computer servers to leverage massive datasets. Optimize machine learning inference on mobile devices.

Telecommuting permitted from anywhere in the U.S.

Minimum Requirements: Bachelor's degree, or foreign equivalent, in Computer Science, Electrical Engineering, or a closely related field plus five years of experience in the job offered or a related occupation.

Special Skill Requirements:

(1) Python and C++ programming for developing complex computer vision applications and computational geometry algorithms, including experience with performance optimization and memory management for processing large 3D datasets. Expertise in scientific computing libraries such as NumPy and OpenCV for sophisticated data manipulation and algorithm implementation (3 years)

(2) SLAM (Simultaneous Localization and Mapping) and Structure from Motion for camera pose tracking and 3D reconstruction. Experience in visual odometry, feature tracking, bundle adjustment, and loop closure detection. Experience implementing EKF-based SLAM solutions and familiarity with state-of-the-art SLAM approaches from research literature (2 years)

(3) 3D Computer Vision and Volumetric Reconstruction, including 3D reconstruction techniques including TSDF (Truncated Signed Distance Function) volumetric fusion, depth data processing, and 3D mesh projection/stitching. Experience with multi-view geometry, camera calibration, and point cloud processing. Experience with specialized 3D vision libraries and implementing algorithms from computer vision conferences (CVPR/ECCV/ICCV) (2 years)

(4) Depth Sensing and Processing: comprehensive experience with depth camera systems like Kinect or LIDAR, depth data infrastructure for mixed reality pipelines, and depth compression schemes. Hands-on experience with depth map filtering, fusion of multiple depth sources, and integration of depth data with RGB information for scene understanding. (2 years)

(5) Mixed Reality Systems for Digital Twins: advanced expertise in developing computer vision components from mixed reality applications with focus on digital twin creation and interaction. Experience with spatial mapping, environment understanding, camera tracking, surface detection, and 3D content placement in virtual environments that mirror physical spaces. Experience with integrating real-world geometry with virtual content for interactive digital twin applications. (2 years)

(6) Advanced Computer Vision Algorithms including camera calibration, pose estimation, feature extraction and matching, homography estimation, optical flow, and multi-view geometry. Experience implementing and optimizing these algorithms for specific application domains and hardware constraints. (3 years)

(7) Neural Radiance Fields (NeRF) and Novel View Synthesis for creating photorealistic 3D representations from 2D images. Hands-on experience implementing and optimizing NeRF variants for different applications and hardware environments. (1 year)

(8) Deep Learning for Computer Vision: specialized experience implementing and optimizing deep learning models for computer vision tasks such as object detection, semantic segmentation, depth estimation, and pose estimation. Experience with frameworks like PyTorch and adapting state-of-the-art architectures for specific computer vision challenges. (2 years)

(9) Embedded Computer Vision: Experience optimizing and deploying sophisticated computer vision algorithms on embedded systems with limited computational resources. Experience with ARM architectures, NVIDIA Jetson platforms (TX2), and techniques for efficient inference on edge devices. Experience implementing real-time vision systems on embedded hardware for applications such as smart cameras and mobile devices. (2 years)

(10) Multi-sensor Integration and 3D Environment Capture: Experience with multi-sensor systems for creating comprehensive 3D digital representations of physical spaces. Experience implementing sensor fusion algorithms, localization, mapping, and environment understanding for indoor and outdoor spaces. Experience developing systems that can perform real-time perception tasks in dynamic environments and create accurate 3D models from multiple data sources. Experience with multi-camera calibration, synchronization, and integration for creating seamless 3D spatial models. (2 years)

EOE, including disability/vets.

Must be legally authorized to work in the U.S. without sponsorship.