岗位分析 · R2R 远程站

R2R 远程站

高级机器学习工程师 Senior Machine Learning Engineer - Reinforcement Learning

公司Path Robotics

薪资面议

岗位性质全职

要求地区全球

经验要求不限

发布时间2026-07-14

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岗位来自公开渠道，时效性以官网为准。反馈失效？

技术

🤖 AI 速读

硕博学历，精通Python和PyTorch/TensorFlow
有真实系统RL部署经验，熟悉MuJoCo/Isaac Gym仿真
需构建sim-to-real管道，实体机器人实验经验加分

完整 JD

岗位描述

Design, implement, and evaluate RL algorithms for robotic control, motion planning, and adaptive behaviors in dynamic, unstructured environments.
设计、实现和评估用于动态、非结构化环境中机器人控制、运动规划和自适应行为的强化学习算法。
Develop and integrate RL policies with robot control systems, ensuring compatibility with hardware constraints and real-time requirements.
开发强化学习策略并将其与机器人控制系统集成，确保与硬件限制和实时性要求兼容。
Collaborate with perception teams to fuse RL with vision, depth, and sensor data for robust decision-making.
与感知团队合作，将强化学习与视觉、深度和传感器数据融合，以实现稳健的决策。
Build and maintain sim-to-real pipelines, including domain randomization and transfer learning techniques.
构建和维护从仿真到实际的管道，包括领域随机化和迁移学习技术。
Conduct experiments on physical robots, including designing safety protocols and monitoring for unexpected behaviors.
对实体机器人进行实验，包括设计安全协议和监控意外行为。
Leverage simulation environments (Isaac Gym, Gazebo, MuJoCo, PyBullet) for large-scale training before real-world validation.
在进行现实世界验证之前，利用仿真环境（Isaac Gym、Gazebo、MuJoCo、PyBullet）进行大规模训练。
Continuously improve model efficiency to operate within compute and latency constraints on embedded robotic systems.
不断提高模型效率，以在嵌入式机器人系统的计算和延迟限制内运行。

岗位要求

Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
计算机科学、机器人学、机器学习或相关领域的硕士或博士学位，或同等实践经验。
Experience developing and deploying reinforcement learning algorithms on real-world systems.
具备在真实系统上开发和部署强化学习算法的经验。
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
精通 Python 和深度学习框架，例如 PyTorch 或 TensorFlow。
Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
熟悉模拟环境（例如 MuJoCo、Isaac Gym）。
Solid understanding of probability, statistics, and optimization.
对概率、统计和优化有扎实的理解。
Experience with training and deploying ML models in production systems.
具备在生产系统中训练和部署机器学习模型的经验。

Design, implement, and evaluate RL algorithms for robotic control, motion planning, and adaptive behaviors in dynamic, unstructured environments.
设计、实现和评估用于动态、非结构化环境中机器人控制、运动规划和自适应行为的强化学习算法。
Develop and integrate RL policies with robot control systems, ensuring compatibility with hardware constraints and real-time requirements.
开发强化学习策略并将其与机器人控制系统集成，确保与硬件限制和实时性要求兼容。
Collaborate with perception teams to fuse RL with vision, depth, and sensor data for robust decision-making.
与感知团队合作，将强化学习与视觉、深度和传感器数据融合，以实现稳健的决策。
Build and maintain sim-to-real pipelines, including domain randomization and transfer learning techniques.
构建和维护从仿真到实际的管道，包括领域随机化和迁移学习技术。
Conduct experiments on physical robots, including designing safety protocols and monitoring for unexpected behaviors.
对实体机器人进行实验，包括设计安全协议和监控意外行为。
Leverage simulation environments (Isaac Gym, Gazebo, MuJoCo, PyBullet) for large-scale training before real-world validation.
在进行现实世界验证之前，利用仿真环境（Isaac Gym、Gazebo、MuJoCo、PyBullet）进行大规模训练。
Continuously improve model efficiency to operate within compute and latency constraints on embedded robotic systems.
不断提高模型效率，以在嵌入式机器人系统的计算和延迟限制内运行。

【岗位要求】
Master’s or PhD in Computer Science, Robotics, Machine Learning, or related field, or equivalent practical experience.
计算机科学、机器人学、机器学习或相关领域的硕士或博士学位，或同等实践经验。
Experience developing and deploying reinforcement learning algorithms on real-world systems.
具备在真实系统上开发和部署强化学习算法的经验。
Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
精通 Python 和深度学习框架，例如 PyTorch 或 TensorFlow。
Experience with simulation environments (e.g., MuJoCo, Isaac Gym).
熟悉模拟环境（例如 MuJoCo、Isaac Gym）。
Solid understanding of probability, statistics, and optimization.
对概率、统计和优化有扎实的理解。
Experience with training and deploying ML models in production systems.
具备在生产系统中训练和部署机器学习模型的经验。