Lead Research Engineer End-to-End Autonomous Driving

4 weeks ago

Bangalore Division, India Krutrim Full time

About Krutrim: Krutrim is building AI computing for the future. Our envisioned AI computing stack encompasses the AI computing infrastructure, AI Cloud, multilingual and multimodal foundational models, and AI-powered end applications. We are India’s first AI unicorn and built the first foundation model from the country. Our AI stack is empowering consumers, startups, enterprises and scientists across India and the world to build their end AI applications or AI models. While we are building foundational models across text, voice, and vision relevant to our focus markets, we are also developing AI training and inference platforms that enable AI research and development across industry domains. The platforms being built by Krutrim have the potential to impact millions of lives in India, across income and education strata, and across languages. The team at Krutrim represents a convergence of talent across AI research, Applied AI, Cloud Engineering, and semiconductor design. Our teams operate from three locations: Bangalore, Singapore & San Francisco. Role Overview We are seeking a highly skilled and motivated End-to-End Autonomous Driving Deep Learning Engineer to join our team. In this role, you will design, develop, and optimize deep learning models for end-to-end autonomous driving systems. You will work on cutting-edge technologies that integrate perception, planning, and control into a unified framework, enabling vehicles to navigate complex environments autonomously. Key Responsibilities Design, develop, and implement end-to-end deep learning models for autonomous driving, from raw sensor input (e.g., cameras, LiDAR, radar) to control outputs (e.g., steering, throttle, braking). Collaborate with cross-functional teams to integrate perception, planning, and control modules into a cohesive autonomous driving system. Train and optimize deep neural networks using large-scale datasets, including real-world and simulated driving data. Develop and implement algorithms for sensor fusion, trajectory prediction, and decision-making in dynamic environments. Evaluate model performance through simulation, closed-loop testing, and real-world deployment. Stay up-to-date with the latest advancements in deep learning, computer vision, and autonomous driving technologies. Contribute to the development of tools and pipelines for data collection, annotation, and model training. Document and present research findings, model architectures, and technical solutions to stakeholders. Educational Qualifications & Experience: Bachelor’s, Master’s, or Ph.D. in Computer Science, Electrical Engineering, Robotics, or a related field. 3+ years of experience in deep learning, computer vision, or autonomous systems. Proven experience in developing end-to-end deep learning models for autonomous driving or related fields. Strong understanding of neural network architectures (e.g., CNNs, RNNs, Transformers) and optimization techniques. Hands-on experience with frameworks such as TensorFlow PyTorch. Familiarity with autonomous driving datasets (e.g., KITTI, nuScenes, Waymo Open Dataset). Technical Skills: Proficiency in Python, C++, or similar programming languages. Experience with sensor fusion techniques and multi-modal data processing. Knowledge of reinforcement learning, imitation learning, or model predictive control is a plus. Familiarity with simulation tools (e.g., CARLA, Gazebo) and robotics middleware (e.g., ROS). Preferred Qualifications Experience with transformer-based models for sequential data processing. Knowledge of graph neural networks (GNNs) for trajectory prediction or multi-agent systems. Familiarity with cloud computing platforms (e.g., AWS, GCP) and distributed training. Publications in top-tier conferences (e.g., CVPR, NeurIPS, ICRA) related to autonomous driving or deep learning.

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