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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

The Imaging and Sensing group develops Depth and Image Sensing systems, including the groundbreaking True Depth camera that powers FaceID and the recently released LiDAR scanner.
This multidisciplinary team of ground breaking engineers is responsible for the architecture, design, development, and validation of these highly complex sensing systems, tailored for our products.

We are looking for a student position to join our caSystem Validation team to collaborate on advanced electro-optical system development.


Contribute to a core technology team designing, developing, and validating advanced solutions for our future products.

Conduct characterization, test, and validation aspects of our cutting-edge sensing systems, spanning from early lab prototypes to complete system maturity.

Design and implement complex lab-based experiments, followed by thorough analysis supported by simulations.

Develop and execute lab automation for products evaluation.

Conduct data-driven analysis to support decision-making processes.

We are looking for a Software Engineer (C++) to join our low-level team to build high-performance solutions for analyzing and processing RAW photos and other media.

You will help develop cross-platform tools that combine low-level engineering and image processing, playing a key role in the companys core offerings.

Come help shape a product with real ownership and impact potential, and contribute to the future of AI-powered photography and videography workflows.


Responsibilities
Design, develop, and own end-to-end software solutions using modern C++, alongside Python and web technologies.
Create proofs of concept and rapidly prototype new technologies and product capabilities.
Write automated tests and optimize software for reliability, performance, and efficiency.
Collaborate with cross-functional engineering teams to build and deliver new tools and solutions.

We are looking for a deep learning engineer experienced with computer vision to join our DL algorithms Team. We are dealing with a variety of tasks such as regression, segmentation, classification and detection while editing millions of images every week for professionals, and holding tens of millions edited images by professionals used as datasets.


Responsibilities
Design, development and bring to production novel, robust and efficient deep learning algorithms in the field of computer vision.
Create proofs of concept for new products and technology.
Evaluate and benchmark different algorithms for unique, proprietary datasets.
Collaborate with team members to define future technology and features.

We are seeking an experienced Backend Team Lead, to build and lead a high-performing backend engineering team while driving critical technical initiatives across our platform. This is a pivotal role where you'll balance hands-on technical contribution with people leadership, shaping both our engineering culture and technical architecture.

You'll own significant parts of our backend infrastructure - from large-scale image processing pipelines and AI model serving systems to distributed cloud architecture and RESTful APIs. As a technical leader, you'll mentor engineers, drive architectural decisions, and ensure our systems scale to handle billions of images while maintaining reliability and performance.

Additionally, you will serve as a Squad Lead, managing a cross-functional business unit that includes both Backend and Frontend engineers, driving end-to-end product delivery.

This role offers the opportunity to directly impact our growth by building the team and systems that power our AI-driven photo editing platform used by professional photographers worldwide.


Responsibilities
Lead, mentor, and grow a team of Backend engineers, conducting 1-on-1s, performance reviews, and career development planning.
Lead a cross-functional squad (Backend + Frontend engineers) as a Squad Lead, driving product initiatives from conception to delivery.
Drive technical strategy and architectural decisions for Backend systems, ensuring scalability, reliability, and maintainability.
Develop and optimize scalable, distributed systems on cloud infrastructure.
Balance hands-on technical work (coding, design reviews, troubleshooting production issues) with team leadership and strategic planning.
Conduct code reviews and establish best coding practices, ensuring adherence to engineering standards across your team.
Monitor system health and lead incident response efforts to ensure high availability of customer-facing services.
Foster a culture of ownership, continuous improvement, and technical excellence within your team.

We are seeking a highly organized and detail-oriented Operations Manager with a strong background in complex project management and a passion for AI technology and photography.

As the Operations Manager you will coordinate outsourced data tagging tasks, acting as a keystone between the product and AI teams to external partners and play a pivotal role in ensuring the seamless execution of tasks, a critical component of our AI algorithms for photographers.

Responsibilities:
Requirement Gathering: collaborate closely with the product and AI team to gather and understand the task requirements.
Task Planning: create comprehensive project plans that outline the technical description, goals, scope, timeline, and resources required for each task.
Task Monitoring: continuously monitor the progress, track success criteria, and address any issues or delays promptly.
Quality Control: conduct regular quality assessments of the annotated data from outsourcing agencies, ensuring alignment with our strict quality benchmarks.
Team Collaboration: act as the primary point of contact between the teams, providing regular updates on project status and addressing any questions or concerns in a positive and collaborative manner.
Enhancement and Refinement: actively seek feedback, both internally and from outsourcing partners, to iterate on and improve the annotation process and outcomes.