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 Yet too often they are forced to rely on outdated tools that fail under modern pressures.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

Work Authorization Requirement: Due to the nature of our business and compliance with federal regulations, all candidates must be a "U.S. Person". Upon hire, you will be required to provide documentation verifying your status as a U.S. Citizen, a lawful permanent resident, or a protected individual under 8 U.S.C. 1324b(a)(3).

Summary

We are seeking a talented ML Researcher / 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.

Responsibilities

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

Requirements

BS with a minimum of 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

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

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