2027 Summer Intern, MS/PhD, Road Understanding, ML Engineer
Core
Designing and building machine learning models for entity-centric lane geometry detection and relational topology decoding to support Level 4 autonomous driving.
Role type
Summer Intern, ML Engineer (Road Understanding)
Builds
Core algorithms for lane geometry and road topology for the Waymo Driver
Domain
Autonomous driving, Computer Vision, Graph Neural Networks
Deliverable
production ML models
Required skills
Python, deep learning frameworks (PyTorch, JAX, TensorFlow), deep learning architecture design, 2D/3D geometry, spatial/relational reasoning
Preferred skills
HD map learning, lane topology estimation, large-scale distributed model training, sensor fusion, autonomous vehicle perception stacks
Technologies
PyTorch, JAX, TensorFlow, Transformers, DETR, GNNs, BEV, MapTR, TopoNet, LaneGAP, Ray, TPU, GPU
Responsibilities
Implement core algorithms for lane geometry and topology decoding; Set up data pipelines and train neural networks across sensor modalities; Create evaluation metrics to benchmark model accuracy and analyze failure cases; Partner with research mentors and engineering teams to evaluate downstream planning impact
Seniority
Intern