Lead Machine Learning Engineer

What you'll work on

• Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data). This includes ensuring data is efficiently loaded, distributed, and processed across large GPU clusters.
• Identify and resolve bottlenecks in the training pipeline, including data loading, preprocessing, model computation, and inter-node communication, to maximize GPU utilization and reduce training time.
• Work with the ML team to develop and refine neural network architectures suitable for autonomy tasks, particularly those handling high-dimensional and sequential sensor data.
• Create and adjust loss functions and training strategies that help the model learn effectively from complex multimodal inputs and improve autonomy performance.
• Configure, monitor, and maintain large-scale distributed training jobs across multiple machines and GPUs, ensuring stability, fault tolerance, and efficient resource usage.
• Implement scalable systems to preprocess, transform, and augment large robotics datasets so that they are suitable for model training.
• Work closely with ML scientists and other engineers to integrate new models, experiments, and training approaches into the production training pipeline.
• Analyze training metrics, model outputs, and experiment logs to assess model performance and guide improvements in architecture, data usage, or training strategies.
• Develop tools and workflows that allow teams to run experiments, track results, and iterate quickly on new model ideas or training approaches.