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    Software Engineer Intern, Robotics - Pittsburgh, United States - Parallel LLC

    Parallel LLC
    Parallel LLC Pittsburgh, United States

    2 weeks ago

    Default job background
    Description
    This job is for (Moss -

    ), posted via Parallel. This is a temporary role. Applicants living in the United States are eligible for this role.
    At moss, we are building highly autonomous small robots for tree nurseries.

    We want to make tree growers' lives easier by automating tree counting and spraying first, which are still very labor-intensive and time-consuming today.

    In the future, we want to build fully autonomous, compact robots that make sustainable farming fun, easy and accessible to all types of farmers.

    And we would love to have you join us and build the future workforce for sustainable agriculturetogether.
    We are now looking for student interns to join us from December March part-time or full-time).

    You will get to wear many hats, learn a lot of new skills, and have a lot of fun in our close-knit, small startup.

    Role
    Write high-quality, performant, and safe code that will run on the robot
    Develop novel ML and perception algorithms to detect trees and estimate the height and thickness of trunks
    Generate synthetic data to train our ML models on using realistic simulators
    Develop automated data labelling pipeline
    Optimize models to run in real-time while consuming minimal resources
    Skills
    Proficient in writing optimized C++ and Python code
    Experience in developing deep learning models
    Familiarity with PyTorch and frameworks like TensorRT & ONNX
    Experience working with point clouds data and related algorithms
    Knowledge of classical computer vision algorithms
    Some experience in GPU programming with OpenCL or CUDA
    Proficiency in Linux CLI tooling and git
    Familiarity with ROS/ROS2 and its tooling (Optional)
    Experience interfacing with sensors like LiDAR, cameras, and GPS (Optional)
    Hands-on experiences with robots and field testing (Optional)

    #J-18808-Ljbffr
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