Forward Deployed Engineer
Forward Deployed Engineer (Robotics Data) acting as the technical bridge between Maxinsights' robotics foundation-model data engine and Embodied AI/World Model research clients. Day to day: translating client needs into dataset specs, orchestrating large-scale simulation data synthesis (Isaac Sim, MuJoCo), and curating/QA-ing multimodal robot data with Python-based pipelines.
- Compensation
- Not disclosed
- Location
- Santa Clara
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Solutions engineering
- Posted
Skills
Must have
- Proficient in Python programming with excellent software engineering literacy
- Proficient with tools like Git, Docker, Shell
- Skilled in large-scale multimodal data processing tools (Pandas, NumPy, Arrow, HDF5, RLDS/TensorFlow Datasets)
- Solid foundation in 3D spatial mathematics: quaternions, rotation matrices, coordinate transforms, FK/IK of robotic arms, sensor intrinsics/extrinsics
- Exceptional technical communication for aligning Specs with AI researchers and non-technical business personnel
- Self-driven with stress tolerance and ownership in a fast-iterating startup environment
Nice to have
- Hands-on experience with physics simulators (Isaac Sim, MuJoCo, PyBullet, Unity/Unreal Engine) or NeRF/3DGS/generative world models
- Understanding of autonomous driving perception/planning/control pipelines or Embodied AI foundation models (e.g., VLA, RT-2)
Full 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.