Sr. Machine Learning Engineer (Perception and Tracking)
Ouster View all jobs
- Ottawa, ON Toronto, ON
- $162,000-180,000 per year
- Permanent
- Full-time
- 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.
- 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.
- 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.).