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We are looking for a highly skilled Senior Sensor & Embedded System Testing Engineer to join our team in Herzliya.
In this role, you will help create the full data lifecycle that underpins our models: from designing what data we collect, through curation and quality monitoring, to running rigorous experiments that drive model improvements. You will work closely with other ML and Data Engineering teams to ensure our models are trained on the best possible data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected.

The selected candidate will assume responsibility for the configuration of advanced camera and laser sensor systems, and for the rigorous validation of their functionality at the embedded level. This role encompasses interface verification, timing analysis, performance characterization, firmware integration support, and direct collaboration with research and physics evaluation programs.

Responsibilities
Lead the configuration and bring-up of camera and laser sensor subsystems within embedded platforms.
Define, develop, and execute comprehensive system-level validation and definition testing strategies.
Characterize sensor performance across optical, electrical, and timing domains.
Validate sensor interfaces (MIPI CSI-2, SPI, I²C, LVDS) and ensure compliance with system specifications.
Support firmware integration activities, ensuring robust sensor operation within the full system stack.
Design and maintain automated test infrastructure for sensor validation and regression testing.
Collaborate closely with physics, optics, and research teams to support experimental evaluation campaigns.
Perform root-cause analysis of issues spanning hardware, firmware, optics, and sensor subsystems.

Management and leadership of technological operations and projects.
Planning and execution of models and integration between technology, intelligence, and field requirements.
Operation and implementation of complex technological systems-including field work.
Matrix task management, leadership, and teamwork.

We are looking for a talented and curious Audio Machine Learning Engineer to join our growing Machine Learning team in Herzliya. In this role, you will help create the full data lifecycle that underpins our models: from designing what data we collect, through curation and quality monitoring, to running rigorous experiments that drive model improvements. You will work closely with other ML and Data Engineering teams to ensure our models are trained on the best possible data, reaching the best accuracy, and that we deeply understand when and why they don't perform as expected.

Redefine the future of human-computer interaction and the way people communicate. Contribute to products that shape mobile computing and create breakthrough technologies in the audio domain.
In this role, you will push the boundaries of audio solutions across the full stack - from data pipelines and model training to optimization for our silicon. You'll collaborate with world-class researchers and engineers to ship technology that reaches hundreds of millions of users, while upholding our unwavering commitment to privacy.

Responsibilities
Work with unique, proprietary datasets - developing algorithms to process them and devising metrics to evaluate and improve quality.
Design and implement machine learning models focused on the audio domain, for low-latency feedback and high-fidelity streaming.
Drive data quality insights and influence the design of our end-to-end system.
Conduct both cutting-edge research and product-oriented development.
Collaborate closely with researchers, engineers, and product teams to bring new capabilities to life.

We are seeking a Cyber Operations Lab Manager for an operative role at the core of key intelligence operations.
This position is responsible for managing critical infrastructures that are essential to the success of operational missions.
The role involves tackling complex technological challenges, leading to significant operational impact, and ensuring the seamless functioning of secure communication networks.
:Key Responsibilities
Manage key infrastructures that are central to operational missions.
Oversee procurement, installation. Configuration, and maintenance of components in secure communication networks.
Ensure firmware and operating systems are up-to-date.
Define requirements for a software project to manage the lab and remotely operate lab components.

Integration into departmental and divisional projects involving the development of systems and providing hardware testing solutions for new products.
Defining test processes and test setups for various hardware testing domains (voltage conversion/ RF/ audio/WIFI, .etc.)
Joining the ATE team and developing automated testing capabilities and additional applications in the LABVIEW environment.
Developing auxiliary tools and applications for divisional use across various development platforms.
Serving as a knowledge hub for test equipment
Leading automation development projects in collaboration wuth external outsourcing partners.

Play a part in shaping the future of human-computer interaction. Contribute to products that are redefining mobile computing and creating breakthrough technologies in conversational AI, speech recognition, and natural language understanding and generation.

We are seeking a passionate and experienced Machine Learning Engineer to join our team. In this role, you will push the boundaries of on-device speech recognition and NLP, working across the full stack - from data pipelines and model training to optimization for our silicon. You'll collaborate with world-class researchers and engineers to ship technology that reaches hundreds of millions of users, while upholding our unwavering commitment to privacy.

Responsibilities
Work with unique, proprietary datasets - developing algorithms to process them and devising metrics to evaluate and improve quality.
Design and implement machine learning models spanning speech recognition and NLP domains.
Drive data quality insights and influence the design of our end-to-end system.
Conduct both cutting-edge research and product-oriented development.
Collaborate closely with researchers, engineers, and product teams to bring new capabilities to life.

Planning, defining, and implementing cyber defense architecture within the division, ensuring alignment with operational. requirements, regulations, and technologies.
Accompanying technological projects, risk analysis, defining security controls, and leading defense solutions in both on-premise and cloud environments.
Conducting Security Design Reviews for systems and infrastructure.
Evaluating and implementing new information security technologies.
Analyzing cyber risks and recommending protective controls.
Writing architecture documents, standards, procedures, and guidelines.
Leading segmentation, Zero Trust, and secure access concepts.
Addressing regulatory requirements and information security standards.
Collaborating with IT, communication, DevOps, cloud, and development teams.
place of employment: center
scope of hours: 100 hours.

Join our team as a Machine Learning Engineer and help shape the future of on-device AI. You'll research, design, and deploy cutting-edge deep learning models optimized for our silicon edge devices, working across the full ML lifecycle alongside hardware, software, and product teams.

We are looking for a talented and motivated Machine Learning Engineer to join our team. You will work within a collaborative, research-driven engineering culture that values innovation and rigor, with the opportunity to build impactful AI products deployed at scale on real devices. We offer competitive compensation, benefits, and opportunities for professional growth.

Responsibilities
Research and design state-of-the-art deep learning models optimized for resource-constrained our silicon edge devices.
Drive projects across the full ML lifecycle, from ideation and experimentation to production deployment.
Collaborate closely with cross-functional teams including hardware, software, and product.
Continuously evaluate and adopt new techniques to improve model performance and efficiency on-device.