2027 Summer Intern, MS/PhD, Software Engineer, Sys Intel & Machine Learning
Waymo · Mountain View, CA, USA
About this role
## Waymo — 2027 Summer Intern (MS/PhD) Waymo is an autonomous driving technology company on a mission to be the world’s most trusted driver. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and is built on extensive real-world driving experience and large-scale simulation. ### Team **Model Optimization & ML Runtime** (within Smart Perception / Machine Learning) is responsible for maximizing the capability, efficiency, and hardware performance of Waymo’s perception and foundation models. The team bridges large-scale ML research (e.g., Vision Transformer backbones, multi-sensor fusion, and many perception heads) with real-time onboard deployment on custom automotive accelerators—developing core optimization frameworks such as parameter-efficient fine-tuning, quantization, compilation, and quantization-aware training. ### You will: - Design, implement, and benchmark **parameter-efficient fine-tuning** (LoRA / QLoRA) modules in **JAX/Flax** for **multi-task Vision Transformer** backbones - Develop **dual-level distillation** pipelines (intermediate feature matching + task-head logit distillation) 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**, ensuring **zero latency overhead** on onboard compute platforms - Conduct extensive empirical ablations and evaluate perception metrics on large-scale autonomous driving datasets across diverse geographic domains ### You have: - Currently pursuing a **PhD or Master’s** in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related technical field - Strong software engineering and deep learning development skills in **Python** and modern frameworks (**JAX, Flax, PyTorch, or TensorFlow**) - Solid theoretical understanding and hands-on experience with **deep learning foundation models**, **Transformer architectures**, and **multi-task learning** - Experience with **model compression**, **parameter-efficient fine-tuning** (e.g., LoRA, QLoRA, adapters), or **quantization** and **knowledge distillation** techniques ### We prefer: - Publication record at top-tier CV/ML conferences (e.g., **CVPR, ICCV, ECCV, NeurIPS, ICLR**) - Hands-on experience with model quantization (**PTQ, QAT, INT8/INT4/MX4**), low-precision numerics, or hardware-aware model optimization - Experience training and scaling large vision backbones or multi-modal models on distributed accelerator clusters (**TPUs / GPUs**) - Familiarity with autonomous driving perception tasks (e.g., **3D object detection, semantics, tracking, pedestrian intent prediction**) ### General perks - Work on challenging problems with direct impact - Competitive compensation packages (including housing/relocation bonus if applicable) - Medical, dental, and vision insurance - Fun intern events and networking opportunities ### Onsite perks - Free breakfast, lunch, dinner, and snacks - Free access to Google shuttles - Onsite gym ### Notes - This is a **hybrid onsite** internship. - Resumes are accepted on a **rolling basis** until the role is filled. - To be considered for multiple roles, apply to each one individually (apply to your top 3 roles). ### Pay (hourly) - **Masters:** $70–$70 USD/hour - **PhD:** $85–$85 USD/hour
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