At Cruise, I led the deployment of a vision based perception system that reduced object detection latency by 45 percent, enabling safer navigation in high traffic urban environments. This milestone required redesigning our primary pipeline to handle streaming sensor data from complex intersections in San Francisco. My work addressed the critical bottleneck of compute overhead on edge devices while maintaining accuracy benchmarks. The challenge necessitated a deep dive into model quantization and kernel optimization, effectively bridging the gap between cutting edge research and scalable real world application on autonomous driving platforms.
During my tenure at NVIDIA, I engineered a distributed training framework using PyTorch that accelerated the convergence of large transformer architectures for autonomous driving simulations by 60 percent. I also spearheaded the integration of a novel sensor fusion module at Cruise, utilizing C++ to process lidar and camera data streams, which increased pedestrian detection recall by 12 percent. My implementation of a reinforcement learning algorithm for adaptive cruise control successfully logged over 50,000 miles of error free automated driving. These projects highlight my ability to translate complex deep learning concepts into robust production code.
Wayve stands out as the industry leader in AV2.0 because of your commitment to end to end deep learning rather than relying on brittle rule based heuristics. My expertise in transformer architectures and sensor fusion aligns with your mission to create adaptable driving brains capable of navigating unseen environments. By leveraging my experience in scaling perception models at Cruise and optimizing compute workflows at NVIDIA, I will contribute immediately to your efforts to generalize autonomous driving capabilities. Your focus on embodied AI mirrors my own technical philosophy regarding the future of robotics and machine learning.
My technical proficiency in Python and C++ coupled with my experience in deploying models to production makes me a strong fit for your engineering team. I am ready to apply my knowledge of reinforcement learning and computer vision to help solve the most pressing challenges in autonomous mobility. Please review my attached portfolio to see the architectural designs behind my published papers. I look forward to discussing how my background in safety critical systems contributes to the ongoing evolution of your core technology during an upcoming interview process at your office.
Best regards,
Keiko Yamamoto
