As a Lead Software Engineer at Avride, you’ll lead the development of decision-making systems for autonomous vehicles, manage a team of engineers, and build large-scale data and ML pipelines to optimize self-driving behavior in real-world scenarios.

• Lead the design, development, and optimization of self-driving car scenarios, ensuring effective obstacle navigation, lane change execution, and decision-making under complex driving conditions.
• Manage and mentor a team of engineers, establishing efficient workflows, processes, and pipelines to prevent scenario degradation and enhance the quality of the self-driving system.
• Oversee the collection, labeling, and preprocessing of extensive datasets for machine learning applications, leveraging tools like Toloka, Python, and distributed computing frameworks.
• Develop and implement machine learning models for classification, motion planning, and decision-making components using Python, PyTorch, Keras, CatBoost, and C++.
• Architect data pipelines for processing petabyte-scale datasets, optimizing resources in cloud and on-premises environments to ensure high computational efficiency.
• Design and implement "good driving" metrics, analyze their performance, and continuously improve upon the vehicle's behavioral algorithms.
• Conduct extensive experiments to identify training targets for improving specific scenarios and optimizing machine learning model performance.
• Collaborate with cross-functional teams to develop and maintain high-performance data infrastructure using Apache Airflow, NiFi, Hadoop, and Spark.
• Research and implement state-of-the-art techniques in autonomous driving, ensuring safety and regulatory compliance.

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• Master's degree in Computer Science, Robotics, or a related field. Foreign equivalent is acceptable.
• Minimum of 5 years of professional experience in software engineering, machine learning, or robotics.
• Expertise in programming languages such as Python and C++, with experience in libraries/frameworks like PyTorch, OpenCV, and CatBoost.
• Proficient in designing and optimizing distributed systems for data processing pipelines
• Hands-on experience with machine learning workflows, including data collection, labeling, model training, and deployment in production.
• Deep understanding of autonomous driving components, including motion planning, decision-making algorithms, and sensor data processing.
• Familiarity with containerization and orchestration tools like Docker and Kubernetes for deploying scalable systems.
• Proven ability to manage and lead engineering teams, driving innovation and ensuring high-quality deliverables.
• Experience in creating performance optimization strategies for both hardware and software systems.

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