Machine Learning Engineer, End-to-end Autonomy
- Support the design and development of ML models or model components for end-to-end autonomous driving: ranging from initial data strategy, design, development, experimentation, evaluation and deployment;
- Able to navigate ambiguities and address uncertainties arising from complex projects involving multiple teams and legacy codebases;
- Writes high-quality code while being rigorous with Machine Learning experimentation;
- Collaborate closely with stakeholders from multiple teams in different time-zones to define interfaces and requirements for an end-to-end stack;
- MS, or higher degree, in related field, or equivalent industry experience
- Professional experience with ML frameworks such as PyTorch, Jax or Tensorflow (PyTorch preferred)
- Experience with data sampling and data curation pipelines for autonomous driving datasets
- Experience in state of the art architectures for end-to-end autonomous driving
- Experience in ML workflows: data sampling and curation, pre-processing, model training, ablation studies, evaluation, deployment, inference optimization
- Python and C++ experience
- Experience with infrastructure for large scale datasets and distributed model training
- Experience working with a modern cloud service (AWS, GCP, Azure etc.)
- Hands-on experience with autonomous driving systems
- Experience with model deployment with NVIDIA stack (e.g. ONNX graphs, TensorRT, profiling)
- Familiarity with recent breakthroughs in ML (e.g. foundation models, pre-training and efficient fine-tuning, multimodal Transformer architectures)
- Knowledge of autonomous driving large scale data curation pipelines
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