Want to know if this job is worth applying to?
Palo Alto, California, United States
Onsite
At Rhoda AI, we’re building the next generation of generalist intelligent robots. We own the full robotics stack from high-performance hardware and robot systems to the infrastructure and state-of-the-art foundation world models that control our robots. Our robots are designed to be generalists capable of operating in complex, real-world environments and handling long-tail edge cases, made possible by our cutting edge research and end-to-end system design. We've raised over $450M and are investing aggressively in model research, infrastructure, hardware development, and manufacturing scale-up to make generalist robotics 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
