2027 Summer Intern, MS/PhD, Software Engineer, Sys Intel & Machine Learning
About this role
Scaling foundation models for real-time onboard vehicle deployment presents complex bottlenecks in hardware performance and execution efficiency that this engineer will address. Waymo needs a software engineering intern to bridge the gap between large-scale multi-task machine learning research and real-time execution across custom automotive accelerators within the Model Optimization and ML Runtime team. The incoming person will design optimization frameworks, implement fine-tuning modules, and collaborate closely with latency and quantization specialists to ensure the Waymo Driver operates smoothly.
Candidates who excel in this position typically combine rigorous academic research backgrounds with practical systems-level coding expertise in deep learning frameworks. Ideal candidates enjoy tackling difficult optimization problems, working with large-scale datasets, and contributing directly to autonomous driving infrastructure while partnering with industry leaders.
What you will do
- Design, implement, and benchmark parameter-efficient fine-tuning modules using JAX and Flax for multi-task Vision Transformer backbones.
- Develop dual-level distillation pipelines involving intermediate feature matching and task-head logit distillation to prevent regressions during data scaling.
- Collaborate with optimization and latency teams to validate static weight folding and low-precision quantization for onboard compute platforms.
- Conduct extensive empirical ablations and evaluate perception metrics using large-scale autonomous driving datasets across diverse geographic domains.
What you need
- Currently pursuing a Master's or PhD degree in Computer Science, Electrical Engineering, Machine Learning, Robotics, or a related technical field.
- Demonstrated strong software engineering and deep learning development skills utilizing Python and modern frameworks such as JAX, Flax, PyTorch, or TensorFlow.
- Solid theoretical understanding and hands-on practical experience with deep learning foundation models, Transformer architectures, and multi-task learning.
- Direct experience working with model compression, parameter-efficient fine-tuning methods like LoRA and QLoRA, adapters, quantization, or knowledge distillation.
Nice to have
- Publication record at top-tier computer vision or machine learning conferences such as CVPR, ICCV, ECCV, NeurIPS, or ICLR.
- Hands-on experience with model quantization including 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 like TPUs or GPUs.
- Familiarity with autonomous driving perception tasks such as 3D object detection, semantics, tracking, or pedestrian intent prediction.
Benefits
- Competitive compensation packages with a housing or relocation bonus if applicable
- Medical, dental, and vision insurance
- Fun intern events and networking opportunities
- Free breakfast, lunch, dinner, and snacks
- Free access to Google shuttles
- Onsite gym access
Skills and tech stack
Questions candidates ask
What is the remote work policy for this internship?
This will be a hybrid onsite internship position located in Mountain View, CA.
What is the pay rate for the internship?
The expected hourly rate is $70 for Masters students and $85 for PhD students.
Which team will this intern join?
The intern will join the Model Optimization and ML Runtime team within Smart Perception and Machine Learning.
What is the hiring process timeline?
Waymo will accept resumes on a rolling basis until the role is filled.