Staff/Senior Staff Research Engineer, Deep Learning

Responsibilities:

• Develop Bird’s Eye View (BEV) perception models for autonomous driving applications
• Develop Vision Language Models for scene understanding and action generation for autonomous driving applications
• Complete ownership of project from ideation, design, developing data requirements, model training, evaluation and deployment
• Research and prototype novel solutions which can help tackle the long-tail of real-world problems in challenging and diverse scenarios (e.g. on highway and local roads, extreme weather conditions, sensor failures, etc)
• Collaborate cross-functionally to ensure model development keeps a real-time focus and operates efficiently in compute-constrained environments
• Rigorous approach to model development: running well-designed experiments, defining suitable training and validation datasets, and evaluating on the right metrics
• Ensure that your work is performed in accordance with the company’s Quality Management System (QMS) requirements and contribute to continuous improvement efforts.
• Ensure team compliance with QMS, monitor quality, and drive process improvements.

Required Skills:

• MS or PhD in CS, EE, mathematics, statistics or related field
• 3+ years of industry experience working on cutting edge deep learning applications
• Expertise with Python, willingness to do some C++ development as needed.
• Deep understanding of machine learning principles and methodologies
• Experience with implementing deep learning models in at least one deep learning framework (PyTorch, Tensorflow, Jax)

Preferred Skills:

• Relevant industry experience (prior work on self-driving vehicles, autonomy, computer vision and/or robotics projects)
• Past experiences in deep learning projects involving object detection, motion tracking or semantic segmentation, vision-language models, end-to-end learning
• In-depth understanding of cutting-edge deep learning techniques and architectures like transformers, CNNs
• Expertise in machine learning or related field demonstrated by patents or publications in relevant venues (CVPR, ICLR, ICCV, ECCV, NeurIPS, AAAI, SIGGRAPH)
• Experience  with ML Ops and best practices for scalable ML deployment