Sr. Machine Learning Engineer (Perception and Tracking)

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.
• 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