דרושים » תוכנה » Real Time Embedded Engineer in Jerusalem

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.

Help shape how AI data centers are powered. At SolarEdge (NASDAQ: SEDG), we're a global leader in smart energy technology, with over 3,000 employees, offices in more than 20 countries, and millions of installations worldwide. Solaredge is expanding its innovation into the fast-growing world of AI data-center power infrastructure. Building on our leadership in solar, storage, and power electronics, we are creating a next-generation Power Ecosystem — integrating Solid-State Transformers (SST), Battery Energy Storage Systems (BESS as UPS), and advanced power-conversion platforms designed for the AI era. What you will be doing:
* You will develop high-quality code solutions for energy management devices and systems.
* You will design, develop and verify embedded SW, both in low level and system level.
* You will work with adjacent teams such as hardware, integration, support and ATE

Country:
Israel

City:
Modiin

We are looking for a skilled and motivated experienced RT Systems Integration & Release Manager to lead an systems integration team and own the end-to-end integration and release process for real-time embedded products.
This role is responsible for defining integration workflows, managing team execution, and ensuring that system releases are stable, predictable, and aligned with quality and delivery commitments.
The ideal candidate combines deep embedded systems expertise, strong people leadership, and disciplined release and process management.
What will your job look like:
Directly manage and mentor a team of RT systems integration and release engineers.
Define, implement, and continuously improve the RT system integration and release management processes.
Establish standard workflows, best practices, documentation, and quality gates.
Ensure consistent execution of integration activities across projects.
Drive alignment to integration processes across firmware, software, hardware, QA, and system validation teams.
Identify integration risks early and implement mitigation strategies.
Lead system-level integration of real-time hardware and software.
Manage system debugging, and root-cause analysis of complex integration issues.
Oversee validation, verification, and system-level testing activities.
Communicate progress, risks, and mitigation strategies to stakeholders and leadership.

We are seeking a Embedded Systems Software Architect to own the technical direction of our embedded software platforms. This role combines hands-on development, system architecture, and technical leadership, guiding a team of embedded engineers while ensuring high-quality, scalable, and reliable software across current and future products.
You will act as the technical authority for embedded software, working closely with hardware, systems, and product stakeholders to translate requirements into robust software solutions.
What will your job look like?
Lead the technical direction and architecture of embedded software platforms across middleware and application layers.
Serve as the technical authority for embedded software, combining hands-on C/C++ development with system-level ownership.
Design scalable, modular software architectures and define clear interfaces between hardware, OS services, middleware, and application software.
Drive key technical decisions, architectural trade-offs, and long-term roadmap.
Develop and integrate middleware and application-layer components, including multi-threaded and real-time logic.
Lead design reviews, mentor embedded software engineers, and promote high coding and architectural standards.
Take ownership of complex system-level debugging, performance optimization, and software quality.
Work closely with OS, systems, product, QA, and manufacturing teams to translate product requirements into robust technical solutions.

The Product Validation department is looking for a Validation Engineer.
Our department is responsible for delivering high-quality ADAS (Advanced Driver Assistance Systems) products.
We are the first line of support for our customers around the globe, and as our Validation Engineer, you will interact with most of our departments to achieve our goals.
What will your job look like:
Perform system-level validation of embedded systems, covering both hardware and software, with a strong focus on functional testing
Design and maintain test environments, including lab setups, test systems, and configurations.
Write and execute test plans and test cases based on system requirements, including manual and automated tests.
Validate communication protocols and system interfaces, and perform hardware bring-up and integration testing.
Install and support debug systems and sensors on development vehicles and assist automotive customers during integration.

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

We arelooking for an Embedded Software Engineer for performance verification and profiling.
You will join the Virtual Platforms and Performance Verification team in the EyeQ Platform Group (EPG), working on current and next-generation ADAS/AV systems.
Virtual platforms, performance models, and profiling tools are core components of the EPG infrastructure. These tools are used during the pre-silicon phase for hardware architecture optimization and software development, and in the post-silicon phase for software performance optimization.
What will your job look like:
Performance verification of EyeQ SoC designs
Development of hardware virtual platforms
Running hardware benchmarks and correlating virtual platform results with silicon measurements
Development of tracing and profiling tools for CPU performance analysis and optimization
Close collaboration with hardware architects and software, OS, and algorithm teams.

A leading company in the wellness and preventive medicine industry is seeking an experienced Back-End Developer to join its growing team.



Responsibilities:

Develop and maintain scalable back-end services using C# and .NET Core
Design and implement RESTful APIs and microservices
Leverage Azure services to build cloud-based solutions
Ensure high performance, security, and scalability of applications
Collaborate closely with front-end, DevOps, and cross-functional teams
Write clean, maintainable, and testable code
Participate in Agile ceremonies (stand-ups, sprint planning, retrospectives)
Troubleshoot and resolve back-end issues efficiently

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.