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Nvidia Machine Learning Engineer Neural Reconstruction 
United States, California 


What you’ll be doing:

  • Collaborative Partnerships: Foster close collaborations with our research scientists and software specialists, transforming innovative research into robust, industrial-strength machine learning models for our autonomous driving platform.

  • Research and Innovation: Delve into the thrilling sphere of deep learning, conducting research and implementing methods for 3D ground truth creation in the context of autonomous driving. Continually keep abreast of the latest developments in machine learning and autonomous driving, incorporating new methodologies, technologies, and solutions that could enhance our system's performance and capabilities.

  • Experimentation: Design and implement experiments, analyzing the results to assess the effectiveness of our solutions. This vital task involves manipulating real-world data, providing insightful findings that directly influence our technology's performance.

  • Documentation: Methodologies, insights, and results must be thoroughly and effectively documented. This crucial step facilitates knowledge sharing, paves the way for future development, and ensures the reproducibility of results.

What we need to see:

  • Education: An MS or PhD in Engineering or Computer Science with a focus on Deep Learning, Artificial Intelligence, or a related field, or equivalent experience.

  • Communication Skills: Excellent written and verbal communication skills, complemented by strong interpersonal abilities.

  • Experience: A minimum of 5+ years of experience applying machine learning to address real-world problems.

  • Programming Skills: Strong Python programming experience, coupled with robust software design skills.

  • Deep Learning Skills: Experience with deep neural network training, inference, and optimization in leading frameworks such as PyTorch, TensorFlow, and TensorRT.

  • Software Engineering Experience with software engineering, particularly in the context of large and complex systems.

  • Strong understanding of the mathematical foundations of ML and deep learning.

  • Attitude: A self-motivated individual with a strong team spirit, possessing an inherent drive to learn new things and solve challenging problems.

Ways to stand out from the crowd:

  • Applied Experience in Autonomous Driving: Evidence of practical experiments and projects within the autonomous driving domain, showcasing your ability to apply machine learning algorithms to solve complex problems in this specific field.

  • Published Research: Particularly around perception tasks for autonomous driving demonstrating a deep understanding and contribution to the field.

  • Advanced Model Knowledge: Proficiency with innovative models such as NeRF, demonstrating your capacity to stay ahead of and maximize the latest innovations in machine learning.

  • Cross-functional Collaborator: Demonstrated experience working effectively with diverse teams, including researchers, software developers, and product managers, to translate complex concepts into tangible solutions.

  • Problem Solver: A track record of using innovative thinking and a problem-solving approach to overcome technical challenges.

  • Adaptable Learner: Evidence of continuous learning and skill development in the constantly evolving field of machine learning and AI. This might include further education, certifications, or a practice of self-learning.

You will also be eligible for equity and .