דרושים » תוכנה » PC application developer

Our Platform SW department is looking for a highly driven and dynamic SW customer engineer to join our team and help shape the future of Autonomous Driving systems.
Our team ensures fast and successful integration of our technologies in customers solutions
(car manufacturers / Tier1), from the design phase to a car launch, while working closely with internal R&D teams. We're looking for a strong, hands-on, software engineer who is passionately motivated by challenges and excels in troubleshooting hard, complex problems.
What will your job look like?
You will work directly with our development teams on integration into the car manufacturer's system from a real-time, safety-critical system aspects.
You will be responsible to provide quick and professional analysis and troubleshooting of advanced customer issues.
Support new boards bring up : coding, integration, unit test and customer support..
Integration with various embedded systems.

The Operating System Team is looking for a highly talented Embedded SW Integration Engineer with a passion for operating systems and Linux in specific.
The team is developing the Boot and Operating System SW for our autonomous vehicle and smart advanced driver assist platforms.
What will your job look like?
Hands-on debugging and testing of OS and related components (Boot, HW initialization, Device drivers, Linux Kernel, User space libraries and tools & automated tests in python).
Deep understanding of the involved OS/Boot components and their integration with other SW/HW modules involving testing automation and system level validation.
Closely work with members of the Boot and OS development teams to integrate, test and automate new features, components and systems.
Release & feature management in a multi-project and multi-disciplinary environment while managing CI systems for testing coverage.

As an ML Software Engineer with a focus on low-level and CUDA-based optimizations, you will play a key role in shaping the design, performance, and scalability of machine learning inference systems. Youll work on deeply technical challenges at the intersection of GPU acceleration, systems architecture, and ML deployment.
Your expertise in CUDA, C/C++, and performance tuning will be crucial in enhancing runtime efficiency across heterogeneous computing environments. Youll collaborate with designers, researchers, and backend engineers to build production-grade ML pipelines that are optimized for latency, throughput, and memory use, contributing directly to the infrastructure powering next-generation AI products.This role is ideal for an engineer with strong systems-level thinking, deep familiarity with GPU internals, and a passion for pushing the boundaries of performance and efficiency in machine learning infrastructure.

Responsibilities
Design and implement highly optimized GPU-accelerated ML inference systems using CUDA and low-level parallelism techniques
Optimize memory, compute, and data flow to meet real-time or high-throughput constraints
Improve the performance, reliability, and observability of our inference backend across diverse compute targets (CPU/GPU)
Collaborate with cross-functional teams (including researchers, developers, and designers) to deliver efficient and scalable inference solutions
Contribute to ComfyUI and internal infrastructure to improve usability and performance of model execution flows
Investigate performance bottlenecks at all levels of the stack-from Python to kernel-level execution
Navigate and enhance a large, complex, production-grade codebase
Drive innovation in low-level system design to support future ML workloads

As a Large Scale Training Engineer, you will play a key role in enhancing the training throughput of our internal framework and enabling researchers to pioneer new model concepts. This role demands excellent engineering skills for designing, implementing, and optimizing cutting-edge AI models, alongside writing robust machine learning code and understanding supercomputer performance deeply. Your expertise in performance optimization, understanding distributed systems, and bug elimination will be crucial, as our framework supports extensive computations across numerous virtual machines.

This role is designed for individuals who are not only technically proficient but also deeply passionate about pushing the boundaries of AI and machine learning through innovative engineering and collaborative research.

Key Responsibilities
Profile and optimize the training process to ensure efficiency and effectiveness, including optimizing multimodal data pipelines and data storage methods.
Develop high-performance TPU/GPU/CPU kernels and integrate advanced techniques into our training framework to maximize hardware efficiency.
Utilize knowledge of hardware features to make aggressive optimizations and advise on hardware/software co-designs.
Collaboratively develop model architectures with researchers that facilitate efficient training and inference.
Design, maintain, and evolve a high-quality, shared codebase that emphasizes correctness, readability, extensibility, testing, and long-term maintainability, while balancing performance requirements.