At Nitro.AI, our team of Machine Learning Engineers engages in advanced research and development focused on enhancing the safety and performance of autonomous driving systems. We tackle intricate challenges that traditional methods struggle to solve, aiming to achieve autonomous driving capabilities that emulate human-level intuition. Collaboration across various departments within Nitro.AI is integral to our approach, ensuring the effective integration of machine learning technologies.

Key Responsibilities

Data Curation and Evaluation: We specialize in crafting high-quality datasets tailored specifically for autonomous driving scenarios and devising robust evaluation metrics to accurately measure algorithm performance.
Active Learning Strategies: Our team explores innovative techniques to efficiently select and label informative data points, minimizing manual labeling efforts while maximizing model performance.
Optimization of Network Architectures: We investigate methods for autonomously discovering optimal neural network architectures tailored to the generation of labels from sensor and video data in autonomous driving applications.
Specialized Learning Techniques: Our efforts include developing strategies to leverage knowledge from related tasks or domains, addressing scenarios with limited labeled data (low-shot learning), and managing class distribution imbalances (long-tail learning) typically encountered in autonomous driving datasets.
Efficient Learning and Inference: We optimize learning algorithms and inference processes to ensure efficient resource utilization, crucial for the real-time deployment of autonomous driving systems. Privacy-Preserving Techniques: We prioritize the development of techniques that preserve privacy while maintaining high-performance label generation, ensuring compliance with privacy regulations and safeguarding user information.

Qualifications

Master's degree (minimum of 3 years of relevant experience) or Ph.D. in Computer Science, Electrical Engineering, Mathematics, Statistics, or a related field pertinent to machine learning, or equivalent practical experience.
Solid background in Linear Algebra, Probability, Signal Processing, and fundamental machine learning concepts.
Proficiency in programming languages such as C/C++, Python, and others.

Preferred Qualifications

Experience in autonomous driving and robotics development, including Object Detection, Semantic Segmentation, Depth Estimation, and Transformer-based models.
Experience in developing and utilizing automated learning pipeline systems.
Basic understanding of LLM/VLMs (Large Language Models/Very Large Models).
Track record of publications or contributions in relevant conferences or journals such as CVPR, ICCV, ECCV, NeurIPS, AAAI, etc.
Passion for exploring and resolving new challenges in the field.

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