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We are seeking a Senior or Principal Backend Engineer for our Tel Aviv R&D center to join the Talon team, which is responsible for our Enterprise Browser. In this role, you will be instrumental in developing a key security solution that protects businesses by isolating web sessions, preventing data leaks, and managing security policies. You will take a leading position within the team, owning and leading feature development from end-to-end and delivering substantial improvements across the entire product to enhance our multi-layered security approach.
Key Responsibilities
Design, implement, test, and deploy significant features for the Enterprise Browser backend services, contributing to all phases of the development lifecycle.
Take full ownership of specific feature segments, demonstrating accountability for quality and timely delivery from conception to release.
Proactively collaborate and exchange information with cross-functional teams to ensure seamless integration of product features and infrastructure.
Drive innovation by contributing ideas and participating in brainstorming sessions to challenge our approach and enhance product capabilities.
Simplify complex technical problems and focus on the details to deliver high-quality, robust, and performant solutions.
Actively share knowledge and mentor other engineers, contributing to a positive and collaborative team dynamic that empowers others.
Continuously seek opportunities for improvement within team processes and the product itself by applying learnings and challenging the status quo curiously and positively.

As a Senior Frontend Engineer on the Cortex Platform DSPM team, you will be instrumental in developing the next generation of cloud security interfaces for our company. This role spans the entire product lifecycle, from initial concept to production, utilizing modern frontend frameworks and leading AI-driven development practices.
We are looking for more than just a frontend developer; we need someone who lives and breathes AI. The ideal candidate has a proven track record of creating AI-powered development workflows, has integrated AI coding assistants into their core toolkit, and possesses a clear, forward-looking vision for how AI will transform software development.Key
Responsibilities
Frontend Development & Architecture: Lead the design and development of high-performance, scalable user interfaces using React or Angular, combined with TypeScript. Contribute significantly to architectural decisions and continuously evolve our core frontend technology stack.
AI-First Development Evangelism: Build, refine, and champion AI-powered development practices, including automated testing, intelligent code reviews, and code generation. Leverage AI coding assistants to significantly accelerate delivery timelines and elevate code quality.
Feature Ownership: Assume complete end-to-end responsibility for major UI features, overseeing their lifecycle from initial architectural planning through to final production deployment.
Cross-Functional Collaboration: Partner closely with Product, UX, and Backend teams to accurately translate complex security needs and requirements into intuitive, seamless user experiences.
Mentorship: Guide and mentor other engineers on best practices for modern frontend development and the implementation of AI-first coding methodologies.
What Makes You Stand Out
You've already built AI-driven workflows - not just used them, but designed processes that others adopted
You have a vision for AI in engineering - you can articulate how AI will change frontend development in the next 2-3 years
You actively experiment with new AI tools and integrate them into your daily workflow
You understand that AI is a tool for amplification, not replacement - and you know how to wield it effectively.

As an Engineering Manager at Cortex Cloud, you will lead a team of ~6 engineers operating in one of our most complex and cross-functional domains. This role requires a strong blend of people leadership and deep technical expertise, including systems design and large-scale distributed cloud architectures. You will be responsible for driving the design and delivery of complex, high-impact systems, while ensuring the team executes against the roadmap with both speed and engineering rigor.
Key Responsibilities
Build, mentor, and lead a high-performing software engineering team, fostering a culture of empowerment and driving both individual growth and collective impact.
Partner closely with Product Management and cross-functional teams (Infrastructure, Research, UX) to define priorities and shape multi-quarter product roadmaps, ensuring alignment across all stakeholders.
Own the end-to-end software development lifecycle, translating product strategy into executable plans and ensuring consistent, high-quality, on-time delivery.
Provide architectural leadership for scalable, distributed systems operating at petabyte scale, guiding the design and implementation of high-throughput, cloud-native backend microservices.
Drive production readiness by enforcing best practices around deployment, observability, reliability, and runtime stability, focusing on the details to ensure operational excellence.
Align stakeholders across business units through clear communication of technical strategy, trade-offs, priorities, risks, and execution plans.
Engage directly with strategic customers to lead technical deep dives and architecture reviews, and to influence future product direction.
Foster a culture of high engineering standards, accountability, and continuous improvement, with a strong emphasis on quality and security.

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 are seeking a researcher to join the LTX Model Applications team, focusing on advancing and extending the capabilities of our LTX-Video models. You will design and explore new use cases, train novel ways to control the generation process, and push the boundaries of generative video technology. Your work will directly influence millions of users through our apps and contribute to the broader research and open-source communities, shaping the next generation of creative tools.

What you will be doing
Advance and extend the capabilities of our LTX-Video models through new controls, features, and use cases.
Design and implement machine learning models for video, image, and audio generation, as well as multimodal applications.
Conduct experiments, prototype concepts, and validate ideas to expand generative video technology.
Collaborate with engineers, designers, and product managers to translate research into impactful product features.
Regularly open-source code, models, and research insights, contributing to the academic and open-source communities.
Stay at the forefront of generative AI research in video, image, and audio, and bring relevant advances into our products.
Write high-quality, efficient code and maintain best practices in research workflows.
Mentor other researchers and lead collaborative projects.

As a Research Scientist in Model Evaluation, you are the ultimate authority on model quality and utility. You will design the automated judges, reward models, evaluation datasets, and benchmarking ecosystems that determine the future of LTX. Your mission is to provide the "ground truth" for our pre-training and post-training teams. You will blend the rigor of a researcher with the intuition of a product-thinker, developing metrics that capture both the aesthetic soul of a video and the functional precision required for high-stakes professional use.

Key Responsibilities
Steer Training & Research: Systematically evaluate model checkpoints to provide actionable insights that guide training experiments and architectural decisions.
Design Benchmark Ecosystems: Develop and run rigorous benchmarks for release candidates against competitive models, ensuring LTX-2 remains world-class.
Build Next-Gen Metrics: Develop robust automatic metrics and Reward Models (e.g., for RL, ITS, auto-research agents) that quantify complex attributes like temporal coherence, physical correctness, spatial accuracy, and foley synchronization.
Diagnose & Analyze: Perform deep root-cause analysis on model failures, providing the diagnostic clarity needed for researchers to implement targeted fixes.
Scale Evaluation: Collaborate with platform engineers to deploy evaluation frameworks across large-scale GPU clusters.

The Sales Engineer acts as the bridge between engineering and business. Responsible for translating customer needs into technical and commercial solutions, supporting Presale and project execution, and developing technical-marketing assets.

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.

This role focuses on pioneering model architecture and pre-training algorithms, shaping the next generation of our foundational generative AI models.

What you will be doing
Pre-train and fine-tune video, audio, and image generative models to pursue state-of-the-art results.
Publish papers and open source models to benefit the research community and advance the field.
Design and implement machine learning models for text-to-audio and text-to-video generation.
Collaborate with data engineers to curate and preprocess text and video data.
Optimize models for high performance, ensuring efficient training and inference.
Build new controls and capabilities into generative text-to-audio and text-to-video models.
Stay updated with the latest developments in Generative AI, particularly in the fields of image, video, and audio.
Work closely with product teams to integrate AI models into applications and services.
Conduct experiments and prototype new concepts to advance the capabilities of our AI tools.

As a Large Scale Video Understanding Research Scientist, you will play a key role in improving video generation quality and efficiency by improving video and audio understanding pipelines used for both training data construction and model evaluation.. This role demands hands-on work with large-scale Video Language Models (VLLMs), including fine-tuning, post-training, and control, alongside implementing classic computer vision and signal processing algorithms and applying strong research skills. Your expertise in post-training and controlling large scale foundational models, understanding statistics, implementing complex systems and eliminating bugs will be crucial, as our video training sets consist of petabytes of data processed across hundreds to thousands of virtual machines.

What you will be doing
Fine-tune and control VLLMs for video and audio understanding.
Design algorithms for balancing, filtering, and curating training and evaluation datasets, informed by model behavior and failure modes.
Implement classic and modern algorithms for processing, clustering, evaluation and filtering of large scale datasets.
Work within high-performance, scalable distributed systems capable of handling petabytes of data, with attention to throughput, correctness, and reproducibility..
Collaborate with other researchers and product stakeholders to iteratively improve training sets and evaluation protocols through tight feedback loops driven by model performance.

As a Site Reliability Engineer on the SASE Platform team, you will play a critical role in building and operating highly available, secure, and globally distributed services. Your mission is to ensure our cloud-native security and networking platform is reliable, scalable, and performant from day one, protecting the users, applications, and data for the world's largest enterprises as they adopt cloud, remote work, and AI
Your Impact:
Proactively collaborate with development teams to embed reliability, scalability, and operability into services from the earliest design stages.
Design, review, and evolve cloud-native architectures to improve availability, performance, cost efficiency, and fault tolerance.
Build and operate automation for provisioning, deploying, and managing global infrastructure using Infrastructure as Code (IaC).
Improve CI/CD pipelines and release processes to enable safe, fast, and repeatable deployments.
Drive observability best practices, including metrics, logs, traces, and SLIs/SLOs to enable data-driven incident analysis.
Participate in on-call rotations, reducing mean time to resolution (MTTR) through automation and proactive reliability improvements.
Challenge existing processes by championing reliability, security, and operational maturity across the organization.

We are seeking a Windows Internals Engineer for our Tel Aviv R&D center. In this role, you will be a key member of the team that designs and develops the low-level Windows components for our revolutionary Cortex XDR endpoint security product, directly contributing to our mission of protecting our digital way of life from advanced cyberattacks.
Key Responsibilities
Contribute to the research and development of Windows drivers, services, and other system-level components.
Challenge the status quo by finding and implementing innovative solutions to counter sophisticated cyberattacks.
Proactively collaborate and exchange information with engineers, security researchers, and product managers to lead projects and initiatives.
Design, develop, and debug complex, high-performance, multi-threaded code in C++.
Conduct in-depth analysis of system internals and focus on the details to produce robust, high-quality security features.
Apply learning from security research and reverse engineering to simplify complex problems and develop practical solutions.

As a Windows Low Level Engineer in Prisma Access, you will architect, design, and build the next-generation Prisma Access agent, redefining secure connectivity for the modern remote workforce. This is a unique opportunity to take charge of a new product architecture from its inception, utilizing up-to-date technology to impact tens of millions of users. You will be instrumental in creating a new approach to connectivity, compliance, and security in a world of loosely defined perimeters and cloud-native applications.
Key Responsibilities
Lead the architecture, design, and implementation of a new, high-impact product from the initial stages.
Design, implement, and test critical C/C++ components, including kernel drivers and user-mode OS-related infrastructures.
Collaborate closely with cross-functional and multi-platform teams to share knowledge and ensure product alignment.
Participate in researching advanced technologies and analyze their implications on connectivity and security.
Investigate and adapt new technologies for integration into the company's product portfolio.
Author comprehensive design documents, development guidelines, and engineering best practices.

As a Principal Mobile Developer in our Tel Aviv R&D center, you will play a pivotal role in architecting a more secure foundation for the enterprise browser landscape. You will join a world-class team of researchers and engineers to shape the core of our mobile enterprise browser, pushing the boundaries of what's possible in security and leaving your mark on its future.
Key Responsibilities
Own critical features from conception to deployment, shaping the core of our mobile enterprise browser.
Develop across multiple disciplines including CI, Mobile, Web, and Security, utilizing various programming languages.
Spearhead research into low-level operating systems to pioneer innovative solutions that enhance enterprise security.
Mentor and guide fellow engineers, sharing expertise to foster a collaborative and high-performing team environment.
Drive continuous improvement in development processes, tools, and overall product quality to champion excellence.
Proactively collaborate and exchange information with other teams to accomplish shared objectives and ensure alignment on security features.
Challenge existing assumptions curiously and positively to identify and solve complex technical problems in the browser security space.

As a Security Research Intern in the Autonomous Attack Disruption team, you will join the frontlines of our Defenders mission to stop attacks in near real-time. Under the mentorship of experienced researchers, you will analyze real-world attacker TTPs (Tactics, Techniques, and Procedures), reasoning over large-scale datasets to write logic that autonomously identifies and disrupts attackers before they can achieve their objectives.

This role requires applied security research expertise, big data analysis capabilities, and engineering skills to deliver production-ready protection at a global scale. This is your chance to see your research findings transformed into live defense logic that protects millions of users.

Responsibilities

Investigate real-world advanced attacker TTPs to support the development of high-fidelity protection logic across complex cross-domain kill-chains.

Apply security expertise to analyze massive telemetry sets using big-data query languages (KQL), reasoning over data to identify novel malicious patterns and engineer evidence-based detection rules.

Contribute to the implementation and coding of automated capabilities that autonomously disrupt sophisticated threats in near real-time.

Assist in the refinement of protection coverage by analyzing real-world attack telemetry to improve the accuracy and performance of existing detection logics.

Contribute to a strategic feedback loop by documenting findings from attack data analysis to improve overall protection logic and system-wide security posture.

Partner with engineering and product teams to translate research insights into production-ready code, helping to validate protection concepts and ship them at a global scale.