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Original posting from Persona AI Inc
Persona AI is developing and commercializing rugged, multi-purpose humanoid robots that perform real work. Persona's founding team has a decades-long history in humanoid robotics, bionics, and product development delivering robust hardware that has touched the stars, worked miles below the surface of the ocean, and even roamed Disney Parks. Our mission is focused squarely on shipping beautiful, reliable products at massive scale, while building a customer-focused team to achieve these aims.
About Us
At Persona we require an unprecedented volume of high-quality, multimodal data. We are moving beyond basic teleoperation to leverage massive datasets of in-the-wild egocentric video combined with dense sensor streams (IMU, haptics, kinematics, and high-fidelity force profiles). We are seeking a highly skilled Data Pipeline Engineer to architect the systems that turn this raw, unstructured multimodal data-including critical force-aware data collections-into high-fidelity training assets for our robots.
The Role
As a Data Pipeline Engineer, you will architect and scale the data infrastructure that feeds our foundation models. Your primary mission is to extract, augment, and align human dexterous manipulation data from massive complex, multi-sensor and egocentric video datasets. Crucially, you will build advanced post-processing algorithms to perform deep force analysis and infer hidden states from raw data-such as processing direct force-torque outputs to quantify grasp dynamics, estimating contact forces from visual cues, extrapolating heavily occluded hand positions, or deriving 3D geometry from 2D frames. You will use spatial, temporal, and cross-modal data augmentation to multiply the value of every minute of data our teleoperation team collects.
Key Responsibilities
- Multimodal Data Pipelines: Architect highly efficient, scalable pipelines to ingest, decode, and synchronously process thousands of hours of high-resolution egocentric video alongside rich sensor streams (IMUs, force-torque sensors, tactile pads, and joint proprioception).
- Force Analysis & Hidden State Inference: Develop sophisticated post-processing algorithms to analyze force interactions and infer unobservable or missing states from raw data. This includes calibrating and cleaning direct force-aware data collections, estimating contact forces from object deformation, tracking occluded objects during complex manipulation, or applying inverse kinematics to fill in missing joint trajectories.
- Kinematic Retargeting & Alignment: Develop algorithms to translate 3D human hand tracking, wrist motion, and pose estimation into the specific 6DoF/joint-space coordinates of our humanoid's end-effectors, relying on sensor fusion to ensure absolute precision.
- Advanced Data Augmentation: Implement robust data augmentation strategies (spatial transformations, temporal scaling, synthetic viewpoints, and sensor noise injection) to expand expert trajectories and improve the robustness of our learning models.
- Teleoperation Synchronization: Work closely with the Hardware Teleoperation Team (UMI & Console operators) to perfectly align human-robot play-data (haptics, force profiles, video, audio, telemetry) with large-scale pre-training datasets.
Required Qualifications
- Education: B.S., M.S., or Ph.D. in Computer Science, Data Engineering, Machine Learning, Robotics, or a related field.
- Programming & ML Frameworks: Deep expertise in Python and extensive experience with PyTorch, specifically in handling custom dataloaders for multimodal datasets.
- Force & Time-Series Data Processing: Experience analyzing and processing complex time-series data from force-torque (F/T) sensors, load cells, or tactile arrays, ensuring pristine alignment with visual frames.
- Video Processing Expertise: Mastery of video processing pipelines and libraries (OpenCV, FFmpeg, Decord) and managing the I/O bottlenecks of terabyte-scale video datasets.
- Computer Vision / Pose Estimation: Hands-on experience with 3D hand tracking, human pose estimation (e.g., MediaPipe), and spatial geometry calculations.
- Embodied AI Familiarity: Strong understanding of modern imitation learning paradigms, VLA architectures, and frameworks focused on human-to-robot transfer (e.g., EgoScale, EgoMimic, or OpenVLA).
- Data Augmentation: Proven ability to implement programmatic and generative data augmentation techniques for computer vision and time-series data.
Bonus Skills
- Experience with NVIDIA's robotic software stack (Isaac, Cosmos, or components of the GR00T framework).
- Familiarity with distributed data processing systems (Ray, Apache Spark) for cluster computing.
- Background in generating or utilizing synthetic robotic data via simulation (Omniverse, MuJoCo).
- Experience integrating spatial awareness or tactile data representations (e.g., Fourier encoding) into visual pipelines.
Why join Persona AI?
- You'll shape technology that's redefining the possibilities of roboti
Source: Persona AI Inc careers