Forward Deployed Engineer

International Recruiting LLC•San Jose, CA

17d

About The Position

The Forward Deployed Engineer will interface directly with Embodied AI and World Model researchers to analyze requirements for large models' input data. This role involves translating research pain points into precise "Dataset Specifications," planning data distribution to cover diverse scenarios, and orchestrating large-scale data synthesis pipelines using 3D simulation engines. Responsibilities include data curation, quality governance, packaging datasets for training pipelines, and establishing a feedback loop for data efficacy. The role also requires continuous cross-team alignment with Algorithm R&D and Platform Development teams to drive improvements in the core data engine.

Requirements

Proficient in Python programming with excellent software engineering literacy (proficient in the use of tools like Git, Docker, Shell, etc.).
Skilled in using large-scale multimodal data processing tools (e.g., Pandas, NumPy, Arrow, HDF5, and RLDS/TensorFlow Datasets structures).
Possess a solid foundation in 3D spatial mathematics, understanding 3D rotations (quaternions, rotation matrices), coordinate system transformations, forward/inverse kinematics (FK/IK) of robotic arms, and sensor intrinsic/extrinsic parameters.
Able to effectively communicate technical requirements, project updates, specifications, and operational issues across stakeholders.
Exceptional technical communication skills, capable of seamlessly aligning complex technical boundaries and delivery Specs with both top AI researchers and non-technical business personnel.
Self-driven; able to maintain a results-oriented mindset and proactively drive projects forward amidst the rapid iterations and ambiguous requirement definitions typical of a startup environment.

Nice To Haves

Hands-on project experience using physics simulators like Isaac Sim, MuJoCo, PyBullet, Unity/Unreal Engine, or NeRF/3DGS/generative world models, is highly preferred.
Understanding autonomous driving perception/planning and control pipelines, or the training and evaluation logic of Embodied AI foundation models (e.g., VLA, RT-2, etc.), is highly preferred.

Responsibilities

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.
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.
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.
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 MCAP, HDF5, or LeRobot formats) to guarantee seamless, direct ingestion into training pipelines.
Take ownership of data delivery and follow up to evaluate data efficacy during the early stages of client model training, establishing a closed-loop "data-to-model performance" feedback mechanism.
Maintain daily, high-frequency communication internally with the Algorithm R&D and Platform Development teams, abstracting system-level bugs or shared requirements discovered during frontline deployment to drive the standardized upgrade of Maxinsights' core data engine.