Senior Machine Learning Engineer (Auto Labeling)

We are looking for the best At 42dot, our Senior Machine Learning Engineers conduct research and development on machine learning algorithms to ensure safe autonomous driving. We address complex challenges that aren't easily solved through conventional means, aiming to achieve human-level natural autonomous driving. Additionally, we collaborate with various teams across 42dot to leverage machine learning effectively. 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 #J-18808-Ljbffr