We are looking for a highly technical Machine Learning Engineer to lead our efforts in Object Detection and Tracking. You will not simply be "importing" pre-made models; you will be architecting deep neural networks, translating state-of-the-art research papers into code, and optimizing these systems for real-time, on-device performance. This role requires a deep knowledge of neural network architectures. You should be confident ripping apart a model to modify layers, loss functions, and data flows to fit our specific constraints. Key Responsibilities - Architect Unified Models: Design and train DNN models that perform Object Detection and Tracking simultaneously, leveraging temporal information to improve consistency. - Research to Production: Evaluate state-of-the-art research papers and prototype these concepts (turning papers into code) and adapt them into robust, production-grade solutions. - Deep Model Customization: Go beyond standard libraries by implementing custom loss functions, modifying internal model architectures, and designing specific data augmentation strategies to squeeze out maximum performance. - Edge Optimization: Ensure high accuracy is matched by high efficiency. Optimize models for real-time inference and on-device deployment. - Data Strategy: Develop training recipes for data-constrained environments and effective post-training strategies. Required Qualifications - Core Stack: - 5+ years proficiency in Python and PyTorch. - 3+ years proficiency in C++ for production deployment and optimization. - Detection & Tracking: Deep theoretical and practical understanding of modern object detectors (e.g., Transformers, YOLO variants, R-CNNs) and tracking algorithms (e.g., DeepSORT, Kalman Filters, Optical Flow). - Architecture Internals: Proven experience not being dependent on "out-of-the-box" APIs. You have a track record of modifying model architectures via extensive experimentation to meet specific requirements. - Low-Data Regimes: Experience improving model generalization with limited data using Transfer Learning, Domain Adaptation, or Few-Shot Learning. - Mathematical Foundation: Strong grasp of linear algebra and probability as it applies to custom loss function design and geometric 3D vision. Preferred Qualifications - 3D / LiDAR Experience: Hands-on experience with 3D Point Cloud data (LiDAR) is a massive plus. - Deployment Tools: Experience with TensorRT, ONNX Runtime, or edge-specific hardware (NVIDIA Jetson, etc.). The base pay will be dependent on your skills, work experience, location, and qualifications. This role may also be eligible for equity & benefits. ($162,000 - $180,000) We acknowledge the confidence gap at Ouster. You do not need to meet all of these requirements to be the ideal candidate for this role.