Machine Learning Researcher / ML-Ops Engineer

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