Senior AI Engineer Self-Supervised Learning

What you’ll be doing

• Design, build, and maintain scalable data pipelines to process, filter, and transform terabytes of raw, multi-modal sensor data (e.g., video, LiDAR, IMU, odometry) from our robotic fleet.
• Develop and implement state-of-the-art self-supervised and representation learning algorithms to automatically extract features, discover patterns, and generate pseudo-labels from our unlabeled data.
• Collaborate closely with the VLA Foundation Model and RL teams to define data requirements, APIs, and strategies for leveraging curated datasets and learned representations.
• Architect and implement robust evaluation strategies, benchmarks, and datasets to rigorously track the performance and quality of both the data pipeline and the downstream models that consume it.
• Own the data integration workflow, creating efficient data loaders and access patterns to make high-signal data readily available for model training and experimentation.
• Research and prototype novel techniques in data curation, active learning, and anomaly detection to continuously improve the quality and efficiency of our data engine.

What you must have

• Master’s degree or higher in a relevant field such as Computer Science, Machine Learning, or Robotics.
• A minimum of three years of industry or research experience, with PhD experience applicable.
• Deep expertise in self-supervised learning (SSL) and representation learning, particularly with multi-modal sensor data (e.g., contrastive learning, masked autoencoders, world models).
• Proven experience in building and managing large-scale data processing pipelines for machine learning (e.g., using Spark, Kubeflow, or similar cloud-native tools).
• Strong understanding of robotic sensor data (e.g., camera, LiDAR, IMU, odometry) and their characteristics.
• Strong programming skills in Python and deep experience with PyTorch, including creating custom and efficient DataLoaders.
• Experience with MLOps best practices and data versioning tools (e.g., DVC, Pachyderm)

Get some bonus points

• PhD degree in Robotics, Engineering, Computer Science, Machine Learning or a similar discipline, or an equivalent amount of research experience.
• Publications at top-tier ML or robotics conferences (e.g., NeurIPS, ICML, CVPR, CoRL, ICLR).
• Experience with generative models (e.g., GANs, Diffusion Models) for data augmentation or simulation.