Staff, Robotics ML/Data Engineer

Persona AI Inc•Houston, TX

7d•Onsite

About The Position

Persona AI is building humanoid robots for demanding environments in heavy industry, performing dangerous and physically demanding tasks. We are seeking a highly skilled AI Engineer to architect the systems that turn raw, unstructured multimodal data into high-fidelity training assets for our robots. This role is critical as it sits at the most leveraged point in our training pipeline, directly impacting the speed and capability of our foundation models. You will architect and scale the infrastructure to process multimodal data, extract, augment, and align human dexterous manipulation data from multi-sensor and egocentric video datasets. This includes building advanced pre-processing algorithms to infer hidden states, reconstructing 3D geometry, and augmenting data to multiply its value. Your work will directly determine how fast our models learn and how far they can go.

Requirements

M.S., or Ph.D. in Computer Science, Data Engineering, Machine Learning, Robotics, or a related field.
Deep expertise in Python and extensive experience with PyTorch, specifically in handling custom dataloaders for multimodal datasets.
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.
Mastery of video processing pipelines and libraries (OpenCV, FFmpeg, Decord) and managing the I/O bottlenecks of terabyte-scale video datasets.
Solid working knowledge of 3D geometry and robotics data: coordinate frames and transforms, rotation representations, camera intrinsics/extrinsics, forward/inverse kinematics, URDF.
Proven ability to implement programmatic and generative data augmentation techniques for computer vision and time-series data.

Nice To Haves

Experience with NVIDIA’s robotic software stack (Open X-Embodiment, DROID, AgiBot World, EgoDex, or similar).
Familiarity with the modern perception toolbox as a user: segmentation (SAM-family), monocular depth, hand/body pose estimation (MANO/SMPL), 6-DoF object pose tracking, point tracking.
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.

Responsibilities

Design cross-modal validation systems to verify consistency between video, proprioception, force/haptic signals, and language annotations.
Orchestrate hand-tracking, segmentation, depth estimation, 3D reconstruction, and pose-tracking modules; retarget human demonstrations into robot trajectories; and run simulation-in-the-loop validation.
Implement robust data augmentation strategies (spatial transformations, temporal scaling, synthetic viewpoints, and sensor noise injection) to expand expert trajectories and improve model robustness.
Develop unified state-action representations across differing embodiments, coordinate frames, rotation conventions, gripper/hand parameterizations, and sampling rates.
Build tooling for researchers to query, visualize, and audit datasets, and translate model-failure analyses into curation rules and re-collection requests.
Architect end-to-end ingestion pipelines for raw, unstructured recordings to produce indexed, queryable, training-ready datasets, including temporal segmentation, metadata extraction, embedding-based retrieval, and annotation workflows.