Machine Learning Engineer, Pony.ai

• BS/MS or Ph.D in computer science, electrical engineering or a related discipline
• Strong programming skills in C/C++ or Python
• Experience on model optimization, quantization or other efficient deep learning techniques
• Good understanding of hardware performance, regarding CPU or GPU execution model, threads, registers, cache, cost/performance trade-off, etc
• Experience with profiling, benchmarking and validating performance for complex computing architectures
• Experience in optimizing the utilization of compute resources, identifying and resolving compute and data flow bottlenecks
• Strong communication skills and ability to work cross-functionally between software and hardware teams

Desirable

• The ML Infrastructure team at Pony.ai provides a set of tools to support and automate the lifecycle of the AI workflow, including model development, evaluation, optimization, deployment, and monitoring
• As a Machine Learning Engineer in ML Runtime & Optimization, you will be developing technologies to accelerate the training and inferences of the AI models in autonomous driving systems
• Identifying key applications for current and future autonomous driving problems and performing in-depth analysis and optimization to ensure the best possible performance on current and next-generation compute architectures
• Collaborating closely with diverse groups in Pony.ai including both hardware and software to optimize and craft core parallel algorithms as well as to influence the next-generation compute platform architecture design and software infrastructure
• Apply model optimization and efficient deep learning techniques to models and optimized ML operator libraries
• Work across the entire ML framework/compiler stack (e.g.Torch, CUDA and TensorRT), and system-efficient deep learning models