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

- **Company:** Waymo LLC
- **Location:** Mountain View, CA, United States
- **Experience:** Internship
- **Salary:** $145,600
- **Contract:** Internship / Graduate position
- **Skills:** Systems Engineering, Computer Vision, Big Data, Software Code Optimization, Python (Programming Language), Machine Learning, Object Detection, Tensorflow, Model Compression, Sensor Fusion, Software Engineering, Web Application Frameworks, QLoRA, Graphics Processing Unit (GPU), Pytorch, Large Language Models, Deep Learning, Discretization, Information Technology, Transformer Architectures, Model Quantization
- **Published:** November 13, 2026
- **Apply:** https://www.adzuna.com/details/5945957928

## About the Role

* 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 computer vision or machine learning 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 (3D object detection, semantics, tracking, or pedestrian intent prediction)

## Description

Software Engineering builds the brains of Waymo's fully autonomous driving technology. Our software allows the Waymo Driver to perceive the world around it, make the right decision for every situation, and deliver people safely to their destinations. We think deeply and solve complex technical challenges in areas like robotics, perception, decision-making and deep learning, while collaborating with hardware and systems engineers. If you're a software engineer or researcher who's curious and passionate about Level 4 autonomous driving, we'd like to meet you.

The subteam for this role will be:

The Model Optimization & ML Runtime team (within Smart Perception / Machine Learning) is responsible for maximizing the capability, efficiency, and hardware performance of Waymo's cutting-edge perception and foundation models. We bridge the gap between large-scale multi-task ML research (Vision Transformer backbones, 30+ perception heads, multi-sensor fusion) and real-time onboard vehicle deployment across custom automotive accelerators, developing core optimization frameworks (parameter-efficient fine-tuning, quantization, compilation, and quantization-aware training) that power the autonomous Waymo Driver.

Waymo interns partner with leaders in the industry on projects that create impact to the company. We believe learning is a two-way street: applying your knowledge while providing you with opportunities to expand your skill-set. Interns are an important part of our culture and our recruiting pipeline. Join us at Waymo for a fun and rewarding internship!

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 and 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