Member of Technical Staff, Rabbit

• Ideal candidates should be familiar with at least two parts of the full lifecycle of model development:
• Acquiring large-scale data, either through scraping or proprietary acquisition, of traditional multi-modal data (text, image, code) and data of a more esoteric nature
• Curating subsets of large datasets to meet product needs while remaining IP compliant, and serving such data to the training compute infrastructure
• Understanding and characterizing the behavior and limitations of existing models
• Developing, implementing, and testing plans to improve such models with cutting-edge, original research
• Utilizing distributed training frameworks on large compute clusters with multi-node GPU and high-speed interconnect
• Tuning inter-node bandwidth over communication protocols like RDMA over Converged Ethernet (RoCE) with software integrations including NVIDIA Collective Communications Library (NCCL), Gloo, or Message Passing Interface (MPI)
• Developing training and fine-tuning pipelines for large models at scale
• Optimizing and deploying models on a variety of homogeneous and heterogeneous hardware on the cloud or at the edge
• This role is ideal for candidates who have two or more years of industry experience

Desirable

• The machine learning team at rabbit prototypes and builds core machine learning systems that make our operating system experience unique, ranging from large language models to multi-modal foundation models
• Your work will be both pragmatic and pioneering: the models you develop will support the day-to-day functioning of our operating system in areas such as computer vision, conversational intelligence, speech recognition, and synthesis
• Furthermore, your work will expand our understanding of how AI interacts with the world in a way that is similar to human interaction
• Maintain, iterate, and deploy machine learning algorithms in production on our cloud systems
• Research and propose novel methods for some of our most challenging problems that require a machine-learning-based solution. Oversee experimental progress through large-scale training and benchmarking pipelines, and formalize them into alpha and beta features of our operating system
• Evaluate third-party machine learning services and cloud computing vendors for their performance. Provide a perspective on which to build in-house, which to benefit from external partnerships
• Independently execute tasks, solve problems, and report to technical leadership, with the goal of leading a broader team of full-time employees and interns in the future