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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.

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.

Play a part in shaping the future of human communication technology. Contribute to a unique multidisciplinary system that models and understands human interaction, redefining what's possible with computer vision, physics, and signal processing at the edge.

In this role, you'll be at the forefront of a one-of-a-kind technical challenge, developing and implementing novel methods for processing and enhancing a proprietary sensor that models human communication. You'll work hands-on with exclusive data: designing the algorithms that process it, and defining the metrics that evaluate and drive its continuous improvement.

Your insights will carry real weight, directly informing sensor decisions and shaping the architecture of the broader system. You'll lead multi-level research efforts to advance a truly unique sensor, drawing on a rich and diverse technical toolkit spanning signal processing, computer vision, physics, and state-of-the-art deep learning. You'll own proprietary data collections using high-end computer vision techniques, studying signals from their raw-level behavior all the way through to their top-level impact on product performance.

This is a role that lives at the intersection of deep research and real-world impact. You'll conduct cutting-edge investigations and translate your findings directly into product outcomes, influencing decisions across the full stack, from hardware choices and algorithmic pipelines to the features that reach the final product.

Responsibilities
Develop and implement novel algorithms for modeling and understanding human communication, combining 2D/3D computer vision, signal processing, and deep learning.
Work with unique proprietary datasets - design large-scale data processing pipelines, define quality metrics, and provide actionable feedback to improve data collection and labeling workflows.
Devise and implement rigorous evaluation frameworks to measure model and data quality, and drive continuous improvement across the system.
Design neural network architectures optimized for SOTA accuracy and computational efficiency.
Stay current with the latest research across computer vision, signal processing, and efficient ML; evaluate and integrate relevant advances into the team's work.
Contribute to internal tooling and best practices for reproducible, scalable ML research and deployment.

We are looking for an ML Engineer to join a research-oriented group developing physics-informed neural networks for advanced sensing and optical measurement systems. The role focuses on training, validating, and improving neural network models that combine data-driven learning with physical modeling, simulation, and experimental measurements.

Responsibilities
Develop, train, and evaluate neural network models for physics-based sensing and reconstruction tasks.
Incorporate physical knowledge into ML models through data generation, model architecture, loss functions, constraints, or regularization.
Work with simulated and experimental datasets, including data. preprocessing, augmentation, validation, and error analysis.
Build robust training pipelines using Python and modern ML frameworks such as PyTorch.
Compare model predictions against ground-truth data from simulations, optical experiments, or measurement systems.
Collaborate with physicists and optical engineers to understand underlying physical models and translate them into ML workflows.
Analyze model performance, identify failure modes, and improve generalization from simulation to real-world data.
Design experiments to evaluate model robustness, sensitivity, uncertainty, and accuracy.

The system engineering team is responsible for developing, integrating, and building advanced sensing systems, combining cutting-edge modules with advanced algorithms across multi-disciplinary domains: electro-optical components, unique physical models, bio-mechanical considerations, mechanical and electrical trade-offs, dedicated SW & FW, large-scale data analysis, and classic and ML-based algorithms.

Responsibilities
Own the complete system life-cycle - from Proof-Of-Concept through preliminary design, detailed architecture, module builds, testability, and delivery to mass-production.
Risk mapping and mitigation activities.
Define data collection requirements.
Signal analysis and trade-off mapping.
System optimization.
Collaborate with multiple HW and SW teams to analyze and optimize overall system performance.
Define and track system specifications.
Lead multi-disciplinary teams to deliver reliable, high-performance system features.

As an ASIC STA Engineer, you will have responsibilities spanning all aspects of SoC design in terms of timing.
Key responsibilities include timing sign-off, STA and sign-off flow development, ownership of Full chip, IP, and block level timing constraints both for regular and custom timing requirements from synthesis to sign-off to achieve sign-off quality timing constraints.
You will closely interact with RTL designer to understand design intent and clock structure, with CAD to understand and develop flow, and with Physical design team to close and sign-off timing. You will also come up with ideas and plans to verify your own timing constraints. You will innovate timing constraints and flow to facilitate timing closure and any potential pessimism or fall outs in timing analysis.

We are looking for a talented and curious ML Data Scientist 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.
Responsibilities
As an ML Data Scientist on this team, you will play a central role in shaping the data that powers our ML models. You will:
Investigate model failures - identify patterns, hypothesize root causes, and work with the team to implement fixes
Own data curation: evaluate, clean, and curate datasets to maximize model training quality
Design and execute experiments end-to-end: from defining the question and data collection escort, through analysis and statistical validation, to presenting clear conclusions and driving implementation
Define data collection strategies - collaborate with others to decide what data we should be collecting and why
Design and maintain monitoring solutions with others to ensure ongoing data quality and integrity at scale