Machine Learning Researcher

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 Yet too often they are forced to rely on outdated tools that fail under modern pressures.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

• 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

• 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

• 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