Who you are 期待中的你
- A systems software engineer who thinks in latency budgets and memory copies
- Equally comfortable in CUDA/TensorRT and in a CAN bus trace
- You take full ownership from kernel configuration to inference output
Responsibilities
- Own the onboard software platform on NVIDIA Jetson: Real-Time Linux configuration, scheduling, and performance tuning
- Deploy and optimize neural network policies for real-time inference: TensorRT, quantization, zero-copy data paths, strict latency budgets
- Implement and maintain the EtherCAT/CAN master and the joint-level communication with the Motor Controller PCBs
- Integrate sensors: IMU drivers, filtering and time synchronization, cameras and additional sensing as needed
- Build the middleware that moves observations and actions between the bus and the policy at loop rate, deterministically
- Develop logging, replay, and introspection tooling for the whole robot software stack
- Work daily with the RL and Sim2Real engineers on the deployment pipeline, and with embedded on the bus API
Requirements
- B.Sc. in Computer Science, Engineering, or a related field
- 8+ years of software engineering with heavy C/C++ focus; deep understanding of modern C++, memory management, and parallelism
- Extensive experience developing and debugging in embedded Linux environments; real-time or low-latency systems experience
- Hands-on experience deploying neural networks on edge platforms (NVIDIA Jetson, TensorRT or equivalent)
- Knowledge of embedded communication protocols: EtherCAT, CAN, SPI, I2C
- Production-grade Python for tooling and pipelines
- Experience with PREEMPT_RT kernels and real-time performance monitoring
Advantages
- Experience with GPU-accelerated services using zero-copy mechanisms to minimize data transfer latency
- ROS 2 experience
- Background in autonomous driving or edge-AI platforms
- Comfortable communicating technical topics in English with international teams
Skills Required
- B.Sc. in Computer Science, Engineering, or a related field
- 8+ years of software engineering with heavy C/C++ focus; deep understanding of modern C++, memory management, and parallelism
- Extensive experience developing and debugging in embedded Linux environments; real-time or low-latency systems experience
- Hands-on experience deploying neural networks on edge platforms (NVIDIA Jetson, TensorRT or equivalent)
- Knowledge of embedded communication protocols: EtherCAT, CAN, SPI, I2C
- Production-grade Python for tooling and pipelines
- Experience with PREEMPT_RT kernels and real-time performance monitoring
- Experience with GPU-accelerated services using zero-copy mechanisms to minimize data transfer latency
- ROS 2 experience
- Background in autonomous driving or edge-AI platforms
- Comfortable communicating technical topics in English with international teams
What We Do
Mobileye is leading the mobility revolution with its autonomous-driving and driver-assistance technologies, harnessing world-renowned expertise in computer vision, machine learning, mapping, and data analysis. Founded in 1999, Mobileye has pioneered such groundbreaking technologies as REM™ crowdsourced mapping, True Redundancy™ sensing, and the RSS™ safety model. These technologies are driving the ADAS and AV fields towards the future of mobility – enabling self-driving vehicles and mobility solutions, powering industry-leading advanced driver-assistance systems and delivering valuable intelligence to optimize mobility infrastructure. Mobileye technology is used in over 170 million vehicles worldwide. In 2022, Mobileye became an independent company while still being majority-owned by Intel. Mobileye’s headquarters and R&D center are based in Jerusalem, with additional offices across Israel and around the world.
Why Work With Us
Our technology enables self-driving vehicles and mobility solutions, powers industry-leading advanced driver assistance systems, and delivers valuable intelligence to optimize mobility infrastructure.
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