Responsibilities

• Develop deep learning-based approaches to improve and advance large language models (LLMs) and large multi-modal models.
• Design and implement machine learning techniques to adapt foundation models to downstream tasks of interest such as robotic manipulation and high-level task planning.
• Construct and curate datasets for large-scale machine learning, for learning from human preferences, and for specific domains of applications.
• Work closely with our research teams to build state-of-the-art robotics demonstrations of your research.

Requirements

• MS or PhD in a foundation-model-relevant field
• 7+ years of experience in research and development
• Knowledge of large language models, foundation models, model parallelism, or distributed training techniques.
• Excellent programming skills in some rapid prototyping environments such as Python or C++
• Expertise with deep learning frameworks such as PyTorch.
• 1 or more publications in top-tier conferences and journals in Machine Learning, Robotics, or related fields (e.g. CVPR, ICCV, ECCV, TPAMI, IJCV, NIPS, ICLR, ICML, IJRR, ICRA, IROS, RSS, ACC, CDC, etc.)

Bonus

• Experience with Docker, Kubernetes, cloud computing, or similar applications
• Experience with data processing, logging, and visualization tools
• Experience with MLOps (model versioning, model and data lineage, monitoring, model hosting and deployment, scalability, orchestration, continuous learning)
• Experience creating orchestration workflows with tools such as Airflow, Kubeflow, or AWS Step Functions
• Experience with DevOps tools (e.g. CI/CD Pipelines, Infrastructure as Code, containers, Agile software development)
• Experience implementing IoT solutions such as edge computing
• Experience with big data batch and real time data processing (e.g. Hadoop, Spark , Presto, Kafka, Kinesis, Flink)
• Proven track record of publications in top-tier conferences and journals in Machine Learning, Robotics, or related fields (e.g. CVPR, ICCV, ECCV, TPAMI, IJCV, NIPS, ICLR, ICML, IJRR, ICRA, IROS, RSS, ACC, CDC, etc.)
• Experience with parallel programming (e.g. CUDA)


These attributes are great to have but not required for our candidates. Candidates who lack these should not be discouraged from applying.