Job Specifications
About Rivet
Rivet is an American company building integrated task systems — fusing hardened hardware with software, sensors, AI, and networking — for industrial workforces and defense personnel. We create capabilities that multiply the effectiveness of every individual and withstand the world’s toughest environments.
We serve the people who build, operate, maintain, and defend our way of life. From technicians and engineers to first responders and service members, they embody the hard work, ingenuity, and meritocratic values that drive Western prosperity. Yet too often they are forced to rely on outdated tools that fail under modern pressures. Rivet exists to reset that priority.
At Rivet, you’ll join a mission-driven team that fuses disciplines to deliver decisive outcomes where they matter most. Whether shaping our technology, strengthening our partnerships, or building our culture, every role here contributes to equipping the front lines with the modern systems they deserve.
Who Thrives Here
People with a deep disdain for bureaucracy, empire building, groupthink, dogma, corporate babble, and wasted time
Teammates who want to work exclusively alongside others at the top of their field
Experienced, no-nonsense professionals who are execution-focused and deliver high-quality solutions above all else
Role Description
We are seeking a talented ML Research Engineer to advance our computer vision and sensor fusion capabilities. This role combines cutting-edge research with practical implementation of machine learning pipelines for imaging, pose estimation, and model optimization. The ideal candidate will have strong expertise in Python, deep learning frameworks, and experience deploying ML models in production environments. You'll explore new ideas, validate them against the state of the art and deliver working prototypes that influence our product and research direction.
Role Objectives
Implement POCs in Python/C++ to validate ML ideas on embedded hardware
Conduct research in imaging and video processing pipelines for AR/VR applications
Document learnings and define clear pathways from prototype to production
Research and implement model optimization techniques for edge deployment
Stay current with latest developments in computer vision and machine learning literature
Prototype novel algorithms and validate performance through experimentation
Design and implement end-to-end machine learning pipelines using PyTorch and TensorFlow Lite
Optimize models for real-time performance on mobile and embedded platforms
Implement MLOps best practices for model versioning, monitoring, and continuous integration
Create scalable data preprocessing and augmentation pipelines
Role Requirements
BS with 5+ years of academic or industry experience in machine learning research or applied ML engineering with shipped or published work (or MS with 2+ yrs of the above)
Proficiency in Python with experience in ML frameworks (PyTorch, TensorFlow)
Experience with ML pipeline development, model deployment, and production monitoring
Knowledge of quantization, pruning, and edge deployment techniques
Research Areas (at least one)
Imaging/Video Pipeline: Experience with computational photography, video processing, or camera systems
Sensor Fusion & Pose Estimation: Research background in multi-sensor data fusion, tracking, or SLAM
Model Optimization: Experience optimizing ML models for mobile/embedded deployment
Foundational Knowledge (preferred understanding)
Camera Systems: Intrinsic/extrinsic calibration, pinhole model, distortion correction, FOV, color science, exposure control, stereo matching
Image Processing: Demosaic, denoising, sharpening, color correction, tone mapping, gamma correction, HDR, super resolution, segmentation, white balance
Computer Vision: Feature detection/matching, optical flow, structure from motion, 3D reconstruction, SLAM algorithms
IMU & Sensor Fusion: 6DOF/3DOF tracking, gyroscope/accelerometer/magnetometer integration, sensor calibration, sensor fusion algorithms
Preferred Qualifications
PhD in Computer Vision, Machine Learning, or related field
Publications in top-tier conferences (CVPR, ICCV, ECCV, NeurIPS, ICML)
Experience with AR/VR or mobile computer vision applications
Knowledge of CUDA programming and GPU optimization
Experience with cloud platforms (AWS, GCP, Azure) for ML workloads
Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines
Experience with distributed training and large-scale data processing