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Maxinsights

Data platform for physical AI training

Forward Deployed Engineer

Data EngineeringConfirmed live in the last 24 hours
Full-timeSanta Clara, CA, USANo salary listedIn-person

About the Job

Requirements

  • Proficiency in Python programming and strong software engineering literacy, including use of Git, Docker, and Shell.
  • Experience using large-scale multimodal data processing tools such as Pandas, NumPy, Arrow, HDF5, and RLDS/TensorFlow Datasets structures.
  • A solid foundation in 3D spatial mathematics, including 3D rotations (quaternions and rotation matrices), coordinate system transformations, robotic-arm forward and inverse kinematics, and sensor intrinsic and extrinsic parameters.
  • Exceptional technical communication skills to align complex technical boundaries and delivery specifications with AI researchers and non-technical business personnel.
  • Ability to work in a self-driven, results-oriented manner and proactively drive projects forward amid rapid iteration and ambiguous requirements.

Responsibilities

  • Communicate directly with Embodied AI and World Model researchers to analyze their models’ input data requirements, including perception camera field of view, LiDAR precision, robotic-arm dynamic constraints, and action trajectory formats.
  • Translate business and scientific research problems into precise dataset specifications that define data dimensions, sensor parameters, control-command action spaces, and metadata formats.
  • Plan and organize target dataset distributions, balancing routine and long-tail scenarios to support generalizability and coverage of out-of-distribution conditions.
  • Analyze historical or public client datasets to identify gaps in geography, lighting, action types, and obstacle distribution, and perform targeted data completion.
  • Use and extend 3D simulation engines or generative world-model tools to orchestrate and run large-scale data synthesis pipelines.
  • Write scripts in Python or Bash to automatically generate customized robot trajectories, multi-angle perception video streams, 3D point clouds, and dynamics states.
  • Curate, filter, and calibrate data, removing invalid records such as physical-collision errors, transient sensor disconnects, and action drift.
  • Write automated quality-assurance scripts to inspect tens of millions of data frames for dynamics reachability, temporal alignment, and label accuracy.
  • Package datasets in formats such as MCAP, HDF5, or LeRobot for direct ingestion into training pipelines.
  • Own data delivery and evaluate data efficacy during early client-model training, establishing a feedback mechanism between data and model performance.
  • Communicate frequently with Algorithm R&D and Platform Development teams, and surface system-level bugs or shared requirements from deployments to drive standardized upgrades to the core data engine.

Desired Qualifications

  • Hands-on project experience with physics simulators such as Isaac Sim, MuJoCo, or PyBullet; Unity or Unreal Engine; or NeRF, 3D Gaussian Splatting, or generative world models.
  • Understanding of autonomous-driving perception, planning, and control pipelines, or training and evaluation logic for Embodied AI foundation models such as VLA and RT-2.
  • Fluency in both Mandarin Chinese and English, with strong written and verbal communication skills in both languages, and ability to communicate technical requirements, project updates, specifications, and operational issues across US- and China-based teams.

About the Company

Maxinsights

Data platform for physical AI training

Maxinsights builds the data layer for physical AI. It collects real-world human activity to create robot-ready training data, handling collection, quality control, annotation, and curation with its own models. Its products include MaxLoop, which scores and corrects data collection as it happens, and MaxGlove, a sensor glove that captures tactile touch and force data. The company operates globally through a network of over 5,000 contractors and serves more than 30 customers, using an end-to-end pipeline from data capture to delivery. Compared with competitors, it combines hardware (MaxGlove) and software (MaxLoop and related models) to provide real-time data quality feedback and a scalable data-collection infrastructure across many countries. Its goal is to enable reliable, robot-ready datasets for physical AI applications by expanding its data collection and software/hardware capabilities worldwide.

Company size
11-50
Company stage
Seed
Total funding
$30M
Headquarters
Not listed
Founded
2024

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