At Rhoda AI, we're building the full-stack foundation for the next generation of humanoid robots — from high-performance, software-defined hardware to the foundational models and video world models that control it. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling scenarios unseen in training. We work at the intersection of large-scale learning, robotics, and systems, with a research team that includes researchers from Stanford, Berkeley, Harvard, and beyond. We're not building a feature; we're building a new computing platform for physical work — and with over $400M raised, we're investing aggressively in the R&D, hardware development, and manufacturing scale-up to make that a reality.

We're looking for Research Scientists and Research Engineers to build the data and evaluation foundations for our video action model. This team owns web-scale video data curation, annotation pipelines, and evaluation methodology — directly determining the quality of the video pretraining distribution and how clearly we can measure model progress. We hire across levels — from MTS-Staff

What You'll Do

• Design and implement scalable curation pipelines for web-scale video pretraining data: ingestion, deduplication, quality filtering, and content classification across internet-scale video corpora

• Develop video-specific annotation frameworks and quality filters — motion quality, scene diversity, action content, temporal coherence — to improve pretraining signal

• Build evaluation frameworks and benchmarks to measure causal video model capabilities: prediction quality, temporal coherence, long-horizon rollout fidelity, and downstream robot task performance

• Research and implement data selection, mixing, and weighting strategies that improve video generation quality and transfer to robotic control

• Deploy and scale vision-language models (VLMs) and video understanding models for automated annotation, filtering, and content scoring at web scale

• Collaborate closely with pre-training and post-training teams to ensure data quality and evaluation methodology drive research decisions

• Track model capability trends across training runs, catching regressions and surfacing improvements early

What We're Looking For

• Strong understanding of data-centric ML and how web video data quality affects large generative model performance

• Experience building large-scale video data pipelines: ingestion, filtering, deduplication, and quality scoring

• Familiarity with video-specific data characteristics: temporal structure, motion quality, scene diversity, and action content

• Solid ML fundamentals with hands-on experience training or evaluating large generative models

• Ability to design evaluations for video generation models that are diagnostic, reproducible, and actionable

• Staff-level candidates are expected to define technical direction and drive research strategy independently; senior/MTS candidates execute complex projects with strong fundamentals and growing scope

Nice to Have (But Not Required)

• PhD or strong research background in ML, computer vision, or a related field

• Experience with large-scale web video dataset curation (e.g., WebVid, HowTo100M, Ego4D, or similar)

• Familiarity with video generation quality metrics (FVD, perceptual quality, motion consistency)

• Experience running VLM or CLIP-style inference at scale for automated video filtering and annotation

• Prior work on evaluation methodology for video generation or world models

• Understanding of how web video data properties connect to downstream robotic action prediction

• Publication record at NeurIPS, ICML, ICLR, CVPR, or related venues

Why This Role

• The video curation and evaluation rigor you build directly determines pretraining quality and research iteration speed for the entire team

• Build the benchmark infrastructure that gives the team an honest signal of model progress toward real robot performance

• High leverage: improvements to data quality compound across every training run

• Work at the intersection of large-scale systems and generative model research with visibility across all model development