Were 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 Youll 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 Were 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
