Bilingual Forward Deployed Engineer (Mandarin/English)

Maxinsights; Santa Clara (CA); On-site; USD 160,000 - 230,000; Full time

Job summary

Maxinsights is hiring a Forward Deployed Engineer (FDE) for robotics data direction. You will bridge our robotics data engine with Embodied AI and World Model R&D teams, focusing on data specs, high-quality multimodal data generation, and rigorous validation.

The role avoids on-site hardware work and centers on software-driven data production and governance. Responsibilities include defining Specs with researchers, designing diverse scenes, extending 3D simulators, and ensuring data quality

Responsibilities

• Interface with Embodied AI and World Model researchers to define dataset specs, translating requirements into precise data parameters.
• Plan dataset distribution and scene design to maximize generalizability and cover out-of-distribution scenarios.
• Orchestrate data synthesis using 3D simulators and generative tools; write Python/Bash scripts to produce trajectories, multi-angle video streams, point clouds, and dynamic states.
• Curate data and implement automated QA to ensure quality across tens of millions of frames and labels.

Job description

Job Description: Job Overview As a Forward Deployed Engineer (FDE - Robotics Data Direction) at Maxinsights, you will serve as the technical bridge connecting our world-leading robotics foundation model data engine with top-tier Embodied AI and World Model R&D teams globally. While traditional FDE roles typically focus on on-site physical hardware installation and driver debugging, our technology at Maxinsights has achieved a high degree of data streaming and simulation. This role is 100% data- and software-driven, involving absolutely no on-site physical hardware deployment or mechanical debugging. Your core mission is to deeply understand clients' academic and commercial requirements, acquire and align dataset specifications (Specs), design and produce high-quality multimodal motion and scene simulation data, execute rigorous data governance, and conduct agile, high-frequency iterative validations both within and outside the team. Your work will directly ensure that the delivered data accelerates the generalizability learning of our clients' robots.

Core Responsibilities

• Client Requirement Interfacing & Specification Definition Communicate directly with Embodied AI and World Model researchers to thoroughly analyze their large models' architectural requirements for input data (e.g., perception camera FOV, LiDAR precision, robotic arm dynamic constraints, and specific action trajectory formats). Translate ambiguous business and scientific research pain points into high-precision "Dataset Specifications" (Specs), explicitly defining data dimensions, sensor parameters, control command action spaces, and metadata formats.
• Data Distribution Planning & Scene Design Plan and organize the distribution of target datasets, balancing routine scenarios with long-tail scenarios (corner cases) to ensure the datasets possess strong generalizability and cover Out-Of-Distribution (OOD) extreme operating conditions. Analyze historical or public datasets provided by clients to identify data gaps in geography, lighting, action types, and obstacle distribution, and perform targeted data completion.
• Data Production & Synthesis Orchestration Utilize and extend Maxinsights' 3D simulation engines (such as Isaac Sim, MuJoCo, etc.) or generative world model tools to orchestrate and run large-scale data synthesis pipelines. Write efficient scripts (Python/Bash) to automatically generate customized robot trajectories, multi-angle perception video streams, 3D point clouds, and dynamics states.
• Data Curation & Quality Governance Responsible for data curation, filtering, and calibration, eliminating invalid data such as physical collision errors, transient sensor disconnects, or action drifts. Write automated QA scripts to perform static and dynamic quality inspections on tens of millions of data frames across dimensions including dynamics reachability, temporal alignment, and label accuracy. Oversee the final format packaging of datasets (e.g., converting trajectory data into