דרושים » תוכנה » CPU Microarchitect/RTL Engineer

As a manager in the RTL/FE CAD team, you have the opportunity to shape the future of chip design and impact our engineers who develop the next generation of our products! You will lead a high-impact team that develops software solutions used by thousands of RTL design and integration engineers globally across all chip families (iPhone, iPad, Mac, Apple Watch, and more).
In this role, you will be a key catalyst in transforming front-end hardware design into a modern, agile discipline. You will spearhead the transition to agentic workflows-redefining how RTL and integration teams operate in the AI era. This role combines deep knowledge of RTL/FE flows with modern software engineering practices, people leadership, and a transformative vision for engineering methodology.

Responsibilities
People Leadership: Lead, mentor, and grow a team of ~20 CAD engineers, driving talent acquisition, technical mentorship, performance management, and career development.
Agentic Flow & Methodology Transformation: Drive the team and broader RTL/FE community toward agentic workflows. Act as an organizational change agent, pioneering modern, agile methodologies that transform daily design and integration practices.
LLM Harness & Agentic Tooling: Own the architecture and execution of the LLM harness for RTL/FE flows (e.g., autonomous violation triage for Lint/CDC/RDC, intelligent connectivity synthesis, and design assist agents). Establish rigorous benchmarks to continually evaluate and elevate workflow accuracy and autonomy.
RTL Integration & Connectivity Tooling: Own end-to-end RTL integration tools, including connectivity generation, SoC assembly automation, IP packaging, and structural checks.
Static Sign-off Checks: Drive methodology, deployment, waiver governance, and sign-off metrics for Lint, CDC, RDC, and related static verification domains.
Roadmap & Milestone Alignment: Define strategic roadmaps and near-term execution plans aligned with multi-project tapeout milestones and long-term organizational goals.
Stakeholder & EDA Vendor Partnership: Collaborate closely with RTL design and integration leads to turn evolving design challenges into robust software solutions. Partner with EDA vendors to resolve escalations and influence tool roadmaps.
Software Excellence: Champion software engineering best practices, modern Python development, scalable cloud architectures, and robust CI/CD infrastructure within CAD.

As a member of our ML group, you will research and build AI systems that change how our silicon is designed. Many of these problems are still open: the right way to frame the problem isn't known yet, and the data is scarce or noisy.

We are looking for a strong researcher who enjoys working closely with design teams to turn open-ended questions into well-posed research problems, and who wants to see the results used in real projects. Success in this role means models and agents that measurably improve designed chip metrics (Power, Performance, and Area) and design turnaround. You will join a growing team of AI and software engineers working with our chip design teams.

Responsibilities
Research and develop AI methods for the physical design flow, from floorplanning and placement through routing and timing closure, combining predictive models, optimization, and generative AI.
Design and build agentic workflows that connect LLMs to EDA tools, design data, and internal infrastructure, to automate and speed up tasks such as flow setup, debug, design exploration, and analysis.
Work with design teams to turn open-ended problems into well-defined research questions with clear metrics, baselines, and success criteria.
Develop robust AI methods for limited, noisy, or imbalanced data, leveraging domain knowledge and advanced learning techniques.
Own data end to end: extract useful signal from EDA tool outputs and design databases, validate and clean it, and build datasets and benchmarks where none exist.
Run careful experiments and evaluations for both models and agents, and report clearly what works and what doesn't.
Work with software engineers and design teams to bring promising prototypes into production flows, and improve them based on feedback from real projects.
Help shape new research directions as our work grows, follow the AI and EDA literature, and bring relevant ideas to the team.

We are seeking a highly skilled and motivated ML Deployment Engineer to join our on-device AI team. In this role, you will take state-of-the-art machine learning models and make them run efficiently on Apple silicon - compressing, compiling, and optimizing them to deliver intelligent experiences that run entirely on-device, with the performance, power, and privacy our users expect.

Responsibilities
Own the model deployment pipeline from trained checkpoint to on-device execution, ensuring models meet product-quality bars for accuracy, latency, memory, and power.
Apply model compression techniques - quantization (post-training and calibration-based), palettization, pruning, and mixed-precision - and validate the accuracy/size/latency trade-offs of each.
Convert and compile models to the Core ML stack and target the Apple Neural Engine, GPU, and CPU.
Analyze and maximize accelerator (ANE) residency; diagnose why operations are rejected or dispatched to fallback compute units, and work with the graph to resolve them.
Profile end-to-end inference on real hardware, identify bottlenecks, and optimize compute-unit placement, memory footprint, and energy consumption.
Collaborate closely with ML research and applied ML to co-design models that are efficient to deploy and to feed hardware constraints back into model architecture decisions.
Build and maintain tooling and automation for reproducible compression, compilation, benchmarking, and numerical validation.
Stay current with the latest advances in efficient ML inference, model compression, and accelerator architectures.

We are looking for a Physics & Optical Simulation Software Engineer to build and scale our simulation platforms and data pipelines that power our physical modeling and sensor research. While the domain involves physics and optics, this is a software-first role: you will architect modular, high-throughput simulation systems that turn theoretical formulations into production-grade software tools. We value strong software engineering discipline, expertise in Python, and the intellectual agility to quickly master new scientific domains and innovate.

Responsibilities
Architect, build, and maintain robust, end-to-end simulation pipelines and scalable developer platforms.
Design modular, high-throughput simulation workflows and infrastructure in Python and modern scientific stacks.
Partner with physicists and optical engineers to translate physical models, wave-propagation algorithms, and numerical solvers into performant, clean software modules.
Optimize simulation frameworks for performance, scalability, data throughput, and maintainability.
Develop verification suites, benchmarking frameworks, and automated regression testing pipelines to validate simulation outputs against ground-truth and physical measurements.
Establish best practices for simulation software: modular packaging, version control, CI/CD, and technical documentation within a research-driven engineering environment.

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

We are seeking an experienced Signal/Image Processing and Machine Learning engineer to join our team. The role focuses on developing innovative, real-time deep learning and signal processing solutions for new sensing and imaging systems, including video restoration. You will be part of an interdisciplinary team developing technologies that will shape future Apple products.
Description
Algorithm and model development:
- Design, implement, and optimize signal processing and machine learning models for sensing and imaging tasks, including video restoration.
- Develop and validate models using real sensor data, simulated data, and public datasets.
- Prepare, filter, process, and analyze large-scale datasets to train and evaluate models.
- Analyze your algorithm's results and benchmark them against leading methods, and theoretical bounds.
- Optimize models for real-time performance, low latency, and resource efficiency on-device.

Sensing and research:
- Develop algorithms that combine classical signal processing with learned models to extract robust information from sensor data.
- Investigate efficient architecture patterns (lightweight backbones, attention mechanisms, recurrent designs) suitable for real-time, on-device inference.
- Explore how algorithmic approaches can mitigate artifacts and limitations of new sensing and imaging systems.
- Stay current with recent advances in signal processing, computer vision, and machine learning.

Collaboration and technical leadership:
- Work closely with ML researchers, SW, HW, sensor, and architecture engineers to translate research ideas into robust models and practical, shipping systems.
- Provide technical guidance and mentorship to junior engineers, and help shape the team's technical direction.

Responsibilities
- Own the end-to-end development of real-time algorithms, from concept and prototyping through validation and deployment.
- Define evaluation methodology, metrics, and failure analysis processes for sensing and imaging algorithms.
- Drive trade-off decisions among accuracy, latency, power, and memory across algorithm, HW, and system constraints.

Play a part in shaping the future of human-computer interaction. We're looking for an Engineering Program Manager (EPM) to turn advanced machine learning in speech, audio and computer vision into features that ship to customers.
You'll work across ML research, data collection, silicon, FW, and the device and companion SW teams that bring these features to our customers. Your work will cover every stage, from the first dataset to the final release.

You'll own the plan for taking ML features from research to release. That covers data planning, model training and evaluation, optimization for our silicon, integration with SW teams, and release. You'll keep teams aligned, make risk visible early, and help leadership decide when model quality, performance and schedule pull in different directions. You'll be comfortable with how uncertain research is, and disciplined about delivering what ships.

Responsibilities
Own the delivery plan for ML features, from research and prototyping through integration, validation and release.
Partner with data teams to build the data strategy and plan, making sure it covers what the models need and supports program milestones: what data, at what scale and coverage, and by when.
Align model milestones with HW builds, pre-silicon validation (simulation and FPGA), and silicon bring-up, ensuring models are ready at each gate.
Lead integration with SW groups, including model handoff, interfaces, integration schedules and release timing.
Align ML teams, MLOps and data science on priorities, compute capacity and dependencies.
Track model quality, latency, memory and power against product targets, and lead trade-off decisions.
Set up model release practices: readiness gates, versioning, regression tracking and post-integration monitoring.
Summarize ML progress into clear, data-driven recommendations for leadership.

Were looking for a software developer with a real passion for hardware. You enjoy the moment when code makes a physical device do something, youre comfortable standing at a lab bench, and you take a problem from a vague request to a working solution that people actually use.

Responsibilities
Design and build macOS tools and applications, primarily in Python and Swift, for hardware test setups, lab research setups, and lab automation.
Develop company-wide tools used by multiple teams for dedicated proprietary hardware research, and support them as real products with real users.
Own problems end to end: understand the need with the people at the bench, design the solution, implement, deploy, and iterate until it works in the lab.
Integrate with instruments, sensors, and custom hardware over USB, serial, Bluetooth, network, and other lab interfaces.
Build supporting infrastructure as needed: cloud dashboards, databases, data pipelines, and reporting for test and research results.
Implement algorithms and data-processing logic that turn raw measurements into usable results.
Support Windows environments when lab equipment or vendor software requires it.
Make advanced AI coding tools a core part of how you build, and help the team move faster with them.
Take part in design and code reviews, and help raise the quality and maintainability of the teams tools.

As part of our Wireless Connectivity group, you will be working on our latest products, both shipped and pre-released HW, developing tests to validate the PHY FW releases.
Youll be joining a dynamic and growing team of Firmware developers.
Youll work collaboratively with other engineering team members to develop and improve our coverage.
This positions primary focus will be creating scalable and robust test systems to support the delivery of new and existing features for a variety of our devices.

We are looking for a candidate who can take our tests and testing environment to the next level.
In this role, you will be working closely with the Wireless Connectivity Firmware engineers, providing a secure, high quality and robust regression supporting the development of firmware solutions. Targeted to increase the performance and stability of the different Wireless solutions.
You should have a strong background in tests and testing environment development, and a good knowledge and experience in Python.
Advanced problem solving and debugging skills should be a given.

Power the Future with us! SolarEdge (NASDAQ: SEDG), is a global leader in high-performance smart energy technology, with over 3000 employees, offices in 34 countries, and millions of products installed in over 133 countries. Our diverse product offering comprises intelligent solar inverters, battery storage, backup systems, EV charging, and complete home energy management ecosystems. By leveraging world-class engineering capabilities and with a relentless focus on innovation, we strive to create a world where clean, green energy from the sun is the primary source of power for our homes, businesses, and just about everywhere we thrive
The IoE group specializes in making SolarEdge devices smarter and more connected. What You Will Be Doing:
* Develop and design high-quality code following best practices to ensure reliability, maintainability, and scalability.
* Optimize and enhance the performance of existing software components.
* Collaborate with hardware engineers to ensure seamless integration between hardware and software.
* Lead technical discussions, code reviews, and mentor junior engineers.
* Participate in the full software development life cycle including concept, design, development, deployment, and support.
* Leverage cutting-edge AI tools and the latest development features to accelerate development, improve code quality, and drive innovation across the team.
* On the Interface team, you'll develop the core infrastructure handling all communication between the device and external systems — cloud, mobile, and other on-site controllers — deployed across millions of devices worldwide.


Country:
Israel

City:
Herzliya