About the job
At Rhoda AI, we are pioneering the development of a comprehensive technology stack for the future of humanoid robotics. Our focus ranges from high-performance, software-defined hardware to cutting-edge foundational models and video world models that govern these systems. Our robots are engineered as versatile generalists, adept at navigating complex, real-world scenarios that extend beyond conventional training environments. Collaborating at the forefront of large-scale learning, robotics, and systems, our research team comprises distinguished experts from renowned institutions such as Stanford, Berkeley, and Harvard. With an impressive funding of over $400 million, we are committed to substantial investments in research and development, hardware innovation, and the scaling of manufacturing processes to bring our vision to life.
Position Overview
We are currently seeking a Senior ML & Data Infrastructure Engineer to take ownership of and enhance our data model training pipeline. This role encompasses the entire lifecycle, from raw data ingestion and storage to sophisticated indexing, retrieval, and throughput optimization at an unprecedented scale.
Key Responsibilities
Design, develop, and scale a robust data infrastructure capable of processing and managing billions of video clips while ensuring reliability, low latency, and cost-effectiveness.
Create and optimize large-scale storage solutions, including cloud object storage and databases, tailored for multimodal datasets.
Develop high-performance indexing and retrieval systems to facilitate rapid dataset querying, filtering, and iteration for both research and production applications.
Establish observability frameworks for data pipelines that encompass monitoring, alerting, failure recovery, and performance enhancements.
Implement intelligent workload distribution and throughput enhancements across distributed compute and storage infrastructures.
Oversee data artifacts, versioning, and lineage to guarantee reproducibility and traceability throughout training cycles.
Create user-friendly internal interfaces and lightweight tools that empower researchers and engineers to explore, query, and analyze extensive datasets efficiently.
Facilitate the integration and scalable deployment of vision-language models (VLMs) within data pipelines for purposes such as screening, enrichment, or metadata generation.
Qualifications
A minimum of 5 years of experience in data infrastructure, distributed systems, machine learning infrastructure, or a closely related field.
Proven expertise in developing and managing large-scale data pipelines and storage solutions.
Strong programming skills in languages such as Python, Java, or Scala, and proficiency with data processing frameworks.
Experience with cloud-based storage solutions and databases, as well as knowledge of multimodal data management.
Ability to work collaboratively in a fast-paced, innovative environment.
