Lead Machine Learning Engineer

Job description

Job responsibilities

• Design and maintain training systems that can process and learn from petabyte-scale multimodal datasets (e.g., video and point cloud data).
• 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.

Job requirements

• Master’s or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a closely related technical discipline.
• Minimum of 5 years of professional experience developing, training, and deploying machine learning models in production environments.
• Hands-on experience training machine learning models across multiple GPUs or compute nodes, including familiarity with distributed training frameworks and large dataset handling.
• Strong programming skills in Python for implementing machine learning models, data pipelines, and training workflows.
• Solid knowledge of core concepts such as neural networks, optimization algorithms, loss functions, model evaluation, and training methodologies.