Software Engineer 2027 Summer Intern (MS/PhD, Sys Intel & Machine Learning)

Waymo, a leader in autonomous driving technology, as a Software Engineer Intern for the summer of 2027. As part of the Model Optimization & ML Runtime team, you will work on maximizing the capability, efficiency, and hardware performance of Waymo's perception and foundation models. You will design and implement modules for parameter-efficient fine-tuning, develop distillation pipelines, collaborate with various teams, and conduct extensive evaluations on large-scale autonomous driving datasets. This internship offers a unique opportunity to apply your knowledge, expand your skill set, and make a meaningful impact in the field of autonomous driving.

About Waymo

Solid theoretical understanding and hands-on experience with deep learning foundation models, Transformer architectures, and multi-task learning

Responsibilities

• Design, implement, and benchmark parameter-efficient fine-tuning modules in JAX/Flax for multi-task Vision Transformer backbones.
• Develop dual-level distillation pipelines to mitigate multi-task regressions during large-scale data scaling.
• Collaborate with model optimization, quantization, and latency teams to validate static weight folding and low-precision quantization.

Preferred

• Experience with model compression, parameter-efficient fine-tuning (e.g., LoRA, QLoRA, adapters), or quantization and knowledge distillation techniques
• Strong software engineering and deep learning development skills in Python and modern frameworks (JAX, Flax, PyTorch, or TensorFlow)
• Currently pursuing a PhD or Master's in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related technical field
• Experience training and scaling large vision backbones or multi-modal models on distributed accelerator clusters (TPUs / GPUs)
• Hands-on experience with model quantization (PTQ, QAT, INT8/INT4/MX4), low-precision numerics, or hardware-aware model optimization
• Publication record at top-tier computer vision or machine learning conferences (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR)
• Familiarity with autonomous driving perception tasks (3D object detection, semantics, tracking, or pedestrian intent prediction)