Machine Learning Researcher / ML-Ops Engineer

Rivet Industries•Palo Alto, CA

66d

About The Position

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.

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
Experience with computational photography, video processing, or camera systems
Research background in multi-sensor data fusion, tracking, or SLAM
Experience optimizing ML models for mobile/embedded deployment
Intrinsic/extrinsic calibration, pinhole model, distortion correction, FOV, color science, exposure control, stereo matching
Demosaic, denoising, sharpening, color correction, tone mapping, gamma correction, HDR, super resolution, segmentation, white balance
Feature detection/matching, optical flow, structure from motion, 3D reconstruction, SLAM algorithms
6DOF/3DOF tracking, gyroscope/accelerometer/magnetometer integration, sensor calibration, sensor fusion algorithms

Nice To Haves

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

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