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What you'll be doing:
Develop and implement hybrid prediction algorithms that integrate classical methods with machine learning models for vehicle behavior and trajectory planning
Optimize existing algorithms for numerical efficiency and real-time application in autonomous driving scenarios
Design and implement machine learning models, including classical ML (e.g., decision trees, SVM) and deep learning architectures (e.g., Transformers, CNNs) for various prediction tasks
Conduct rigorous testing and validation of prediction algorithms against diverse datasets and in simulated environments
Collaborate with cross-functional teams to integrate prediction models into the overall autonomous driving stack
Stay current with the latest advancements in AI and machine learning, particularly in the context of autonomous vehicles
What we want to see:
MS or PhD in Computer Science, Engineering, Mathematics, or a related field with a focus on machine learning, AI, or robotics
Strong programming skills in C++, Python, and experience with machine learning frameworks (e.g., TensorFlow, PyTorch)
5+ years of experience in developing and implementing both classical and machine learning-based prediction algorithms
Solid understanding of classical prediction experiences, numerical optimization techniques, and their application in autonomous systems
Proven expertise in machine learning, including classical ML algorithms and deep learning architectures
Experience with data analysis, feature engineering, and model evaluation techniques
Excellent problem-solving skills and ability to innovate in the field of algorithmic and ML-based prediction
Strong communication skills and ability to work effectively in a collaborative team environment
Ways to stand out from the crowd:
Experience with productization of AI/ML technology, especially in the automotive or robotics industries
Familiarity with real-time system constraints and the ability to optimize algorithms for computational efficiency
Knowledge of vehicle dynamics and control systems
Contributions to open-source projects or publications in relevant peer-reviewed journals or conferences
Experience with simulation tools and environments for autonomous driving
Familiarity with sensor fusion techniques and processing of multi-modal data (e.g., LiDAR, camera, radar)
As a Prediction Engineer combining classical and ML approaches, you'll play a crucial role in advancing our autonomous driving technology. Your work will directly contribute to improving the decision-making capabilities of our vehicles, enhancing safety, and pushing the boundaries of what's possible in autonomous systems.Join our team and be at the forefront of innovation in AI and autonomous driving. We offer a challenging and rewarding environment where your expertise in both classical and machine learning prediction methods will shape the future of transportation. Apply now and help us navigate the complex landscape of autonomous vehicle technology with cutting-edge prediction capabilities.
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