Waymo · Posted 2w ago
Staff Machine Learning Engineer (Generative Model Performance & Efficiency)
Job relayed by Stapply Data, its source: you apply there.
- Location
- 🇺🇸 United States
- Contract
- Full-time
- Role
- ML engineering
- Source
- Stapply Data
Stack
- PyTorch
- TensorFlow
- Python
- C++
The posting
Join Waymo, a leader in autonomous driving technology. As a Staff Machine Learning Engineer, you will work on cutting-edge simulations for testing and training the Waymo Driver. You will analyze model architectures, optimize code for hardware accelerators, and design low-latency serving solutions. Enjoy a comprehensive benefits package, including medical, dental, and vision insurance, competitive compensation, and a hybrid work model.
- Analyser les architectures de modèles et identifier les goulets d'étranglement dans les performances d'entraînement et d'inférence. - Appliquer et développer des techniques telles que la quantification, l'élagage, la distillation des connaissances et les mécanismes d'attention efficaces. - Optimiser le code du modèle pour des accélérateurs matériels spécifiques (TPUs, GPUs), en tirant parti des fonctionnalités du compilateur et des bibliothèques de bas niveau.
- Strong programming skills in Python and potentially C++, with experience in software development best practices - Proficiency in JAX, Flax, and potentially TensorFlow/PyTorch - 5+ years of experience with deep learning architectures (especially Transformers, Diffusion Models, MoEs), algorithms, and optimization techniques - MS or PhD in Computer Science, Machine Learning, Robotics, or a related field - Hands-on experience with quantization, pruning, distillation, and other model compression methods - Expertise in using profiling tools (e.g., XProf, Perfetto, NVIDIA Nsight) to diagnose performance issues in ML workloads - Familiarity with ML compilers like XLA and an understanding of how they translate high-level code to efficient hardware instructions - Knowledge of TPU and GPU architectures and how to optimize code for them - Experience contributing to frameworks and libraries that improve training speed and scalability (e.g., JAX, Gemax, XManager) - Understanding of concepts related to training and serving models across multiple devices and machines