RoboForce
Senior Software Engineer, Physics Simulation
RoboForce - Milpitas, California, United States, 95035
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Overview
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Overview
We are seeking a Senior Software Engineer to enhance physics simulation tool for robotic autonomy. You will build foundational tools to help develop contact rich robot learning stacks, filling the gap between simulation and real world.
Responsibilities:
Build large scale synthetic data generation pipeline, work closely with AI team on iterating data quality. Build large scale photo-realistic and physics-based simulator. Investigate and address sim to real gaps. Stay current with and apply the latest advancements and technologies in humanoid robotics and simulation. Qualifications:
Bachelor's or Master's degree in Computer Science, Robotics, or related field. Proficiency in modern C++ (C++14/17/20) and Python. Experience in GPU accelerated physics and rendering. Familiarity with machine learning techniques and optimization methods to enhance simulation accuracy and bridge the sim-to-real gap. Experience in system-level optimization through multi-threading, asynchronous programming, concurrency, and parallelism. Proficiency with one or more physical simulators (e.g., MuJoCo, IsaacSim, Drake, PyBullet, PhysX) and experience working in a deployed robotics environment. Requires 5 days/week in-office collaboration with the teams. Preferred Skills:
Expertise in deployed robotics environments. Experience in reinforcement learning, imitation learning or motion planning and control systems.
Responsibilities:
Build large scale synthetic data generation pipeline, work closely with AI team on iterating data quality. Build large scale photo-realistic and physics-based simulator. Investigate and address sim to real gaps. Stay current with and apply the latest advancements and technologies in humanoid robotics and simulation. Qualifications:
Bachelor's or Master's degree in Computer Science, Robotics, or related field. Proficiency in modern C++ (C++14/17/20) and Python. Experience in GPU accelerated physics and rendering. Familiarity with machine learning techniques and optimization methods to enhance simulation accuracy and bridge the sim-to-real gap. Experience in system-level optimization through multi-threading, asynchronous programming, concurrency, and parallelism. Proficiency with one or more physical simulators (e.g., MuJoCo, IsaacSim, Drake, PyBullet, PhysX) and experience working in a deployed robotics environment. Requires 5 days/week in-office collaboration with the teams. Preferred Skills:
Expertise in deployed robotics environments. Experience in reinforcement learning, imitation learning or motion planning and control systems.