2027 Summer Intern, MS/PhD, Road Understanding, ML Engineer
Waymo · Mountain View, California
About this role
**Waymo — 2027 Summer Intern (MS/PhD), Road Understanding, ML Engineer** Waymo is an autonomous driving technology company on a mission to be the world’s most trusted driver. The Waymo Driver powers our fully autonomous ride-hail service and can be applied across vehicle platforms and product use cases. **Software Engineering** builds the brains of Waymo’s autonomous driving technology—helping the Waymo Driver perceive the world, make the right decisions, and deliver people safely. --- ## What you will do - **Design and build machine learning models**: Write clean, high-performance code to implement core algorithms for entity-centric lane geometry detection and relational topology decoding (e.g., merges, splits, predecessor/successor connectivity). - **Run experiments and training pipelines**: Set up data pipelines and train neural networks across vehicle sensor modalities and map priors, using techniques like proxy auto-encoding and prior-dropout to handle real-world challenges (e.g., construction zones and occlusions). - **Benchmark and analyze performance**: Create structured evaluation metrics to benchmark model accuracy and topological correctness across complex intersections; analyze failure cases and iterate on architectural designs. - **Collaborate cross-functionally**: Work closely with research mentors, your buddy, and upstream/downstream engineering teams to evaluate downstream planning impact and package insights for publication or internal deployment. ## What you have - Currently pursuing a **Master’s or PhD** in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related quantitative discipline. - Strong programming proficiency in **Python** and experience with modern deep learning frameworks (**PyTorch, JAX, or TensorFlow**). - Hands-on experience designing, training, and debugging deep learning architectures for **Computer Vision, 3D Perception, or Graph Neural Networks** (e.g., Transformers, DETR-based detectors, GNNs, BEV perception). - Solid foundational knowledge of **2D/3D geometry**, coordinate transformations, and spatial/relational reasoning. ## We prefer - A track record of **publications** in top-tier conferences in machine learning, computer vision, or robotics (e.g., CVPR, ICCV, ECCV, NeurIPS, ICLR, ICML, ICRA, CoRL, AAAI). - Experience with **vectorized HD map learning**, lane topology estimation, or dynamic roadgraph modeling (e.g., MapTR, TopoNet, LaneGAP, or similar). - Experience with **large-scale distributed training** and data infrastructure (e.g., TPU/GPU clusters, Ray, Jax/Flax, multi-GPU pipelines). - Familiarity with **autonomous vehicle perception stacks**, sensor fusion (camera, LiDAR), and downstream motion planning constraints. --- ## Notes - **Hybrid onsite internship**. - Resumes accepted on a **rolling basis** until the role is filled. - If applying to multiple roles, apply to each one individually—please apply to the **top 3 roles** you’re interested in. **Pay (hourly):** - **MS:** $70/hr - **PhD:** $85/hr
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