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3D Spatial Vision Intelligence Engineer

3 years

0 Lacs

new delhi delhi india

Posted:5 days ago| Platform: Linkedin logo

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Skills Required

vision imaging development localization latency reconstruction design estimation tracking algorithms verilog fusion data lidar ai networks segmentation deployment prototyping coding timing verification research visualize power xilinx geometry opencv robotics stack constraints electrical engineering logic architecture algebra probability visual odometry optimization gtsam quartus synthesis dma testing software benchmarking

Work Mode

On-site

Job Type

Full Time

Job Description

Spatial Intelligence


3D Spatial Vision Intelligence Engineer

What You Will Do

  • Architect the Vision Chip:

    Design and implement FPGA-based architectures to accelerate core spatial tasks: depth estimation, feature tracking, and point-cloud processing.

  • Hardware-Accelerated SLAM:

    Port, optimize, and "harden" state-of-the-art SLAM algorithms (e.g., ORB-SLAM, VIO, LOAM) from C++ into RTL (Verilog/VHDL).

  • Sensor Fusion at the Edge:

    Develop high-speed data pipelines to fuse multi-modal inputs (LiDAR, Stereo, IMU) directly on the FPGA to achieve sub-millisecond latency.

  • AI-Hardware Co-Design:

    Collaborate with our AI team to optimize 3D neural networks (like PointNet++ or 3D Segmentation) for deployment on custom hardware.

  • RTL to Reality:

    Manage the full FPGA lifecycle—from HLS prototyping and RTL coding to timing closure, verification, and on-device deployment.

Who You Are

  • The Bridge:

    You can read a SLAM research paper and visualize exactly how those matrix operations should be pipelined in hardware.

  • FPGA Power User:

    Mastery of

    Verilog/VHDL

    and toolchains like

    Xilinx Vivado

    or

    Intel Quartus

    . Experience with

    HLS (High-Level Synthesis)

    is a major plus.

  • 3D Geometry Expert:

    Deep understanding of the math that powers vision—quaternions, transformation matrices, epipolar geometry, and Kalman filters.

  • C++ Expert:

    Strong proficiency in modern C++ and experience with libraries like

    OpenCV, PCL, or Eigen

    .

  • Experience:

    3+ years in computer vision or robotics, with a proven track record of deploying algorithms on embedded hardware or FPGAs.


3D Spatial Vision Intelligence Engineer

Below are the detailed qualifications categorized by priority.

Educational Background

  • Minimum:

    Bachelor’s or Master’s degree in

    Electrical Engineering (EE), Computer Engineering (CE), Robotics

    , or a related field.

  • Preferred:

    A

    PhD

    with a research focus on

    Computational Imaging, SLAM, or Hardware Acceleration

    for Computer Vision.

  • Key Coursework:

    Digital Logic Design, Computer Architecture, Linear Algebra, Probability & Stochastic Processes, and Computer Vision.
Technical Skills: The "Spatial Intelligence" Stack

These are the core competencies required to design a chip that "sees" and "understands" 3D space.

1. SLAM & 3D Perception

  • Algorithmic Expertise:

    Deep understanding of

    Visual-Inertial Odometry (VIO)

    , LiDAR SLAM (e.g., LOAM, LeGO-LOAM), and Visual SLAM (e.g., ORB-SLAM3).

  • Geometric Math:

    Mastery of

    SO(3) and SE(3) Lie Groups

    , quaternions, epipolar geometry, and bundle adjustment.

  • State Estimation:

    Hands-on experience with

    Extended Kalman Filters (EKF)

    , Unscented Kalman Filters (UKF), and Factor Graph Optimization (GTSAM/Ceres).

2. FPGA & RTL Engineering

  • Hardware Description Languages:

    Expert-level

    Verilog

    or

    SystemVerilog

    (preferred) or VHDL.

  • Design Tools:

    Proficiency in

    Xilinx Vivado

    , Intel Quartus, and

    High-Level Synthesis (HLS)

    for rapid algorithm prototyping.

  • Architectural Knowledge:

    Experience with

    AXI protocols

    , DMA, and memory controllers (DDR4/HBM) to handle high-bandwidth 3D point cloud data.

  • Verification:

    Experience with UVM/OVM or hardware-in-the-loop (HIL) testing to ensure "first-time-right" silicon logic.

3. Software & Optimization

  • Primary Languages:

    High proficiency in

    Modern C++ (14/17/20)

    for performance-critical systems and

    Python

    for algorithm modeling.

  • Vision Libraries:

    Strong experience with

    OpenCV

    ,

    PCL (Point Cloud Library)

    , and Eigen.

  • Acceleration:

    Familiarity with

    SIMD

    instructions or

    CUDA

    (for benchmarking FPGA performance against GPU equivalents).

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