Senior Software Engineer - Robot Compute Platform 机器人计算平台软件开发工程师
About this role
At Mentee Robotics, we are redefining humanoid automation with an AI-first approach - combining perception, reasoning, and dexterous manipulation into fully autonomous systems that continuously learn and adapt.
在 Mentee Robotics，我们以 AI 优先的理念重新定义人形机器人自动化——将感知、推理与灵巧操作融合为能够持续学习与自适应的全自主系统。
We are now expanding with a new robotics Engineering Center in China, working hand-in-hand with our engineering teams in headquarters. Its mission: to rapidly develop our next-generation full-size humanoid and bring it to life - a walking, working platform that becomes the foundation of our next generation of products. This is a small, senior, hands-on team where speed of iteration is the core value.
我们正在中国设立全新的机器人工程中心，与总部的工程团队紧密协作。其使命是：快速研发我们下一代全尺寸人形机器人并使其落地——一个能行走、能工作的平台，成为我们下一代产品的基础。这是一支精干、资深、亲力亲为的团队，迭代速度是其核心价值。
We are looking for a Senior Software Engineer to own the robot's onboard compute platform: running neural network policies on Jetson at control rate, and the entire interface to the embedded layer - EtherCAT/CAN master, sensors, IMU, and Real-Time Linux. You are the person who turns a trained policy into a robot that moves.
我们正在寻找一位高级软件工程师，全面负责机器人的车载计算平台：在 Jetson 上以控制频率运行神经网络策略，以及与嵌入式层之间的整个接口——EtherCAT/CAN 主站、传感器、IMU 与实时 Linux。你就是那个把训练好的策略变成会动的机器人的人。
\nWho you are 期待中的你- A systems software engineer who thinks in latency budgets and memory copies
- 一位以延迟预算与内存拷贝来思考的系统软件工程师
- Equally comfortable in CUDA/TensorRT and in a CAN bus trace
- 在 CUDA/TensorRT 与 CAN 总线抓包之间都游刃有余
- You take full ownership from kernel configuration to inference output
- 从内核配置到推理输出，你都全权负责
- Own the onboard software platform on NVIDIA Jetson: Real-Time Linux configuration, scheduling, and performance tuning
- 负责 NVIDIA Jetson 硬件上的软件平台：实时 Linux 配置、调度与性能调优
- Deploy and optimize neural network policies for real-time inference: TensorRT, quantization, zero-copy data paths, strict latency budgets
- 部署并优化用于实时推理的神经网络策略：TensorRT、量化、零拷贝数据通路、严格的延迟预算
- Implement and maintain the EtherCAT/CAN master and the joint-level communication with the Motor Controller PCBs
- 实现并维护 EtherCAT/CAN 主站，以及与电机控制器 PCB 的关节级通信
- Integrate sensors: IMU drivers, filtering and time synchronization, cameras and additional sensing as needed
- 集成传感器：IMU 驱动、滤波与时间同步、相机以及按需的其他传感
- Build the middleware that moves observations and actions between the bus and the policy at loop rate, deterministically
- 构建中间件，以控制环频率在总线与策略之间确定性地传递观测与动作
- Develop logging, replay, and introspection tooling for the whole robot software stack
- 为整个机器人软件栈开发日志记录、回放与内省工具
- Work daily with the RL and Sim2Real engineers on the deployment pipeline, and with embedded on the bus API
- 每天与强化学习及 Sim2Real 工程师协作部署流水线，并与嵌入式团队协作总线 API
- B.Sc. in Computer Science, Engineering, or a related field
- 计算机科学、工程或相关专业学士学位
- 8+ years of software engineering with heavy C/C++ focus; deep understanding of modern C++, memory management, and parallelism
- 8年以上以 C/C++ 为主的软件工程经验；深入理解现代 C++、内存管理与并行计算
- Extensive experience developing and debugging in embedded Linux environments; real-time or low-latency systems experience
- 具备在嵌入式 Linux 环境中开发与调试的丰富经验；具备实时或低延迟系统经验
- Hands-on experience deploying neural networks on edge platforms (NVIDIA Jetson, TensorRT or equivalent)
- 具备在边缘平台（NVIDIA Jetson、TensorRT 或同类）上部署神经网络的动手经验
- Knowledge of embedded communication protocols: EtherCAT, CAN, SPI, I2C
- 了解嵌入式通信协议：EtherCAT、CAN、SPI、I2C
- Production-grade Python for tooling and pipelines
- 能编写生产级 Python 用于工具与流水线
- Experience with PREEMPT_RT kernels and real-time performance monitoring
- 具备 PREEMPT_RT 内核与实时性能监控的经验
- Experience with GPU-accelerated services using zero-copy mechanisms to minimize data transfer latency
- 具备利用零拷贝机制构建 GPU 加速服务以最小化数据传输延迟的经验
- ROS 2 experience
- 具备 ROS 2 经验
- Background in autonomous driving or edge-AI platforms
- 具备自动驾驶或边缘 AI 平台背景
- Comfortable communicating technical topics in English with international teams
- 能够用英语与国际团队就技术话题进行交流
Company at a glance
Mobileye pioneers physical AI for intelligent systems including advanced ADAS, autonomous driving solutions, and humanoid robotics.
Mobileye’s physical AI foundation reflects decades of experience in real-world driving intelligence, applying the same engineering principles that shaped its automotive AI leadership to AI for humanoid robotics in complex physical environments.
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