Staff Robotics ML/Data Engineer — Multimodal Pipelines
Persona AI Inc
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Benefits offered by this job
Job summary
Persona AI Inc in Houston, TX is seeking a Staff Robotics ML/Data Engineer to architect and scale end-to-end robotics data pipelines. You will turn raw egocentric video and sensor streams into high-fidelity training assets for foundation model training, with hands-on, pipeline-focused work to improve data quality and researcher visibility.
The role emphasizes cross-modal validation, multimodal module orchestration, and scalable data ingestion to enable rapid research iterations across multiple
Qualifications
- MS or PhD in Computer Science, Data Engineering, ML, Robotics, or related field.
- Deep expertise in Python and PyTorch with multimodal dataloader experience.
- Experience processing time-series data from sensors with alignment to video.
- Strong video processing with OpenCV, FFmpeg, and Decord.
- Solid knowledge of 3D geometry, transforms, URDF, and kinematics.
- Experience with data augmentation for CV and time-series data.
Responsibilities
- Develop cross-modal validation systems across video, proprioception, force/torque sensors, and language annotations.
- Orchestrate multimodal modules for hand tracking, segmentation, depth estimation, 3D reconstruction, and pose tracking.
- Implement data augmentation to improve model robustness.
- Create unified state-action representations across embodiments and coordinate frames.
- Build dataset tooling and audit workflows with visualization and annotation review UIs.
- Develop end-to-end ingestion pipelines for raw recordings and produce training-ready datasets.
Skills
Education
Tools
Job description
Persona AI Inc in Houston, TX is seeking a Staff Robotics ML/Data Engineer to architect and scale end-to-end robotics data pipelines. You will turn raw egocentric video and sensor streams into high-fidelity training assets for foundation model training, with hands-on, pipeline-focused work to improve data quality and researcher visibility.
The role emphasizes cross-modal validation, multimodal module orchestration, and scalable data ingestion to enable rapid research iterations across multiple
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