Senior Machine Learning Engineer

Responsibilities

• Dataset and Evaluation: We focus on curating high-quality datasets tailored to autonomous driving scenarios and designing robust evaluation metrics to accurately assess algorithm performance.
• Active Learning: Our team explores techniques for efficiently selecting and labeling informative data points, minimizing labeling efforts while enhancing model performance.
• Network Architecture Search: We investigate methods for autonomously discovering optimal neural network architectures, specifically tailored for label generation from sensor and video data in autonomous driving contexts.
• Transfer/Low-shot/Long-tail Learning: Our efforts include developing strategies to leverage knowledge from related tasks or domains, addressing scenarios with limited labeled data (low-shot learning), and handling class distribution imbalances (long-tail learning) commonly found in autonomous driving datasets.
• Efficient Learning and Inference: We optimize learning algorithms and inference processes to ensure resource-efficient utilization, crucial for real-time deployment in autonomous driving systems.
• Privacy: Our team prioritizes the development of privacy-preserving techniques, ensuring the handling of sensitive data while maintaining high-performance label generation, in compliance with privacy regulations and safeguarding user information.

Qualifications

• Master’s or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or a closely related field with relevance to machine learning.
• At least 7 years of hands-on experience in developing machine learning models and pipelines.
• Deep knowledge of Linear Algebra, Probability, Signal Processing, and machine learning fundamentals.
• Advanced programming skills in C/C++, Python, and related libraries/frameworks (e.g., PyTorch, TensorFlow).

Preferred Qualifications

• Extensive experience in autonomous driving or robotics applications, especially in Object Detection, Semantic Segmentation, Depth Estimation, and Transformer-based models.
• Expertise in designing and implementing automated learning pipelines for large-scale systems.
• Strong research background with publications in top-tier conferences/journals (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, AAAI).
• Proven ability to handle large-scale datasets and innovate solutions for rare and challenging edge cases.
• Passion for problem discovery and creative problem-solving in the field of autonomous systems.

Interview Process

• Application Review - Coding Test - 1st Interview - 2nd Interview - 3rd Interview - Offer
• The process may vary by position and is subject to change
• Schedule and results will be communicated via the email provided in your application