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Cynet is looking for an experienced Windows C++ Developer to join our award winning Windows EDR team!


What will you do:

* As a core member of the Windows development group, you will take part in designing and implementing drivers, services, and low-level components of Cynet’s EDR agent.
* You will work in a complex multithreaded environment, collaborate closely with security researchers, and develop innovative detection and protection capabilities across Windows OS internals.
* You will own features end-to-end from design and architecture, through hands-on implementation, performance tuning, and integration into our production environment.

About Us:
Cynet is a leader in threat detection and response, designed to simplify security for organizations of all sizes. Our mission is to empower lean security teams and their partners with an AI-powered, unified platform that autonomously detects, protects, and responds to threats - backed by 24×7 security experts. With a Partner First mindset , we focus on helping customers and partners stay protected, operate confidently, and achieve their goals. Our vision is to give every organization true cybersecurity peace of mind, providing fast, accurate protection without the noise or complexity.

we are seeking a talented data Scientist to join our team. In this role, you will develop and implement ML models to support risk assessment and decision-making processes. The ideal candidate combines strong analytical skills with practical experience in building and deploying Machine Learning models in a financial or fintech environment. Responsibilities:
* Develop, validate, and implement Machine Learning models (Regression, Classification, Supervised/Unsupervised).
* Analyze large datasets to extract insights and support decision-making.
* Collaborate with cross-functional teams to integrate models into production systems.
* Apply Gen-AI techniques where applicable to enhance model performance.
* Document model development processes and results.

required QA Web Engineer.

As a DevOps Manager , you will lead our Cloud infrastructure team. Building and maintaining scalable, secure, and cost-efficient infrastructure. You will drive the adoption of modern DevOps practices, ensuring alignment between business objectives, engineering workflows, and operational excellence. This leadership role requires a balance of hands-on expertise and strategic vision, empowering your team to deliver reliable, high-quality solutions across a fast-paced, dynamic environment. You will play a pivotal role in shaping Fiverrs cloud engineering strategy - optimizing costs, enhancing system reliability, and implementing innovative technologies (including Generative AI-driven automation and insights) to improve productivity and decision-making.

What am I going to do?:

* Lead and mentor a team of DevOps engineers, fostering a culture of collaboration, ownership, and continuous improvement.
* Design, implement, and maintain scalable, secure, and cost-efficient DevOps architectures.
* Continuously evaluate and adopt emerging DevOps tools and best practices to enhance operational efficiency and product quality.
* Drive adoption of Infrastructure as Code, observability, automated testing, and deployment strategies.
* Monitor infrastructure cost efficiency and reliability, providing data -driven insights to leadership.
* Guide the team in exploring and applying GenAI-native tools (e.g., for automating documentation, anomaly detection, self service for developers and performance optimization).
* Partner with cross-functional leaders to influence architecture, operations, and development workflows.
* Report to the VP of Cloud Engineering, contributing to Fiverrs long-term technology roadmap.

We are seeking a Data Engineering Lead to own and scale data platform infrastructure

Build our new data Lakehouse to support various product and business needs
Support cutting-edge AI Agents and cybersecurity use cases
Be part of the Data and AI Algorithms group
Collaborate closely with dev teams, AI/ML engineers, security researchers and business teams
Ensure the availability, reliability and quality of our data infrastructure
Help define best practices for data modeling and orchestration at scale

We're seeking an individual who not only possesses technical skills but also boasts a strong intuition for UX and product considerations. If you thrive on collaborating closely with software engineers and product managers, we want you to join our team.



Responsibilities:

Take responsibility and lead the development of multiple components of the system in a dynamic environment.
Design, implement, and innovate features using cutting-edge technologies.
Design, implement, and innovate end-to-end major features from frontend to backend.
Work closely with product managers, architects, and software engineers to deliver the best customer and user experience.
Lead the growth of the frontend infrastructure, both individually and by working with others.
Promote high-quality design and testing of frontend components and infrastructure.

This is a research‑first role focused on deeply understanding LLM internals to improve the security of AI agents. Youll design careful experiments on activations and interpretable features- e.g., probing, attribution & ablation/patching, representation‑geometry analyses-to uncover mechanisms behind jailbreak, indirect prompt injection, and other attacks. Then translate those insights into signals that can be used for detection and analysis of a model response.

The field of LLM interpretability at scale is exploding, with several major publications in the last months, and major opportunities for innovation.



What Youll Do

Investigate model internals, including activation/features analysis, unsupervised clustering, discovery of directions in latent space, etc. It may also require training specific model parts to improve interpretability metrics.
Design security‑grounded evaluations: curate datasets for different attack types, evaluate performance of different white box (model internals) methods compared to black box (input/output only) baselines.
Publish and share: produce Zenity Labs posts and open artifacts; when the work is strong, aim for tier‑1 ML venues (NeurIPS, ICML, etc.) and security forums. A publication of code and/or trained models in cases of community relevant novelty.
Build tools: Several open source libraries exist (like Anthropics attribution graphs infra), but the research in the field is very dynamic, which will require you to build and adapt tools to your own research directions. This also includes agents to automate research work and distill knowledge from designed experiments.

We are looking for a hands-on technical leader to drive the design, implementation, and evolution of our cloud infrastructure. Youll take ownership of building scalable, reliable, and secure systems that empower engineering teams to deliver with speed and confidence. Youll operate in a fast-paced environment, balancing innovation and pragmatism, and fostering a culture of continuous improvement and operational excellence.



Responsibilities:

Lead end-to-end technical initiatives to design and manage cloud infrastructure across AWS and other multi-cloud environments.
Build and evolve automation frameworks and internal tools using Python, Bash, and modern infrastructure-as-code technologies (Terraform or equivalents).
Architect, implement, and maintain CI/CD pipelines that streamline delivery and improve developer productivity.
Champion observability, reliability, and performance establishing best practices around monitoring, alerting, and system health visibility.
Collaborate closely with cross-functional engineering teams to enable scalable deployments, efficient development workflows, and resilient production systems.
Drive technical tradeoff decisions that balance speed, cost, and reliability in a dynamic, growth-oriented environment.
Act as a mentor and advocate for DevOps culture, enabling teams to take ownership of infrastructure and operations.

As a Senior MLOps Engineer , you will design, build and scale our machine learning infrastructure. You will lead the end to end model lifecycle, from data and training to deployment and monitoring, ensuring reliable and high performance ML systems in production. You will work closely with our research and engineering teams to bring advanced AI models into production and help shape the future of our AI capabilities.