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The computer vision team is working to transform hematology into a big-data science, ultimately enabling better healthcare. We are seeking a senior research engineer to join us in making this revolution a reality.

Your responsibilities will include:
Contributing expertise to the team to extract maximum value from available visual data. Serving as a knowledgeable resource, capable of respectfully teaching and guiding others, while continuously learning and growing.
Researching and implementing AI technology, including deep learning and classical computer vision approaches. Designing and developing efficient, clear and production-ready Python code to ensure rapid and seamless product integration.
Actively participating in researching new directions: sharing ideas and best practices, defining data requirements, contributing to improving the team's internal research tools, and supporting other team members with their research efforts.

Required Data Science Engineer
About the Team
Our team focuses on delivering cutting-edge, AI-powered automated application security features within our ASPM product. We are a dynamic, agile team emphasizing seamless transitions from design to production, deployment, and ongoing monitoring. We excel at operationalizing machine learning, data science, and AI solutions into robust, secure, and high-performance security capabilities.
What You Will Do
Be a core member of a cross-functional AI innovation team, collaborating closely with software engineers, MLOps specialists, and analysts.
Design, build, and optimize end-to-end AI-driven features - from experimentation and model training to validation and deployment.
Develop production-grade code (Python and C#) to integrate advanced models and data-driven capabilities into our core products.
Continuously evaluate model performance and data quality in real-world settings, proactively driving improvements, retraining, and robust monitoring.

Your role will involve leading features from inception and analysis, through design, implementation, testing, and maintenance.
You will also participate in product-changing architectural choices, take ownership of various components/tools, and continuously expand your skill sets.
Expect to immerse yourself deeply in complex sub-systems that support serving of AI models at scale (for example - model-monitoring and feature-store).
We are a leading AI and machine learning company based in Herzliya. Recognized for helping enterprises deliver enterprise-wide analytics at scale, our advanced Machine Learning Operations (MLOps) pipeline enables clients to streamline and manage their AI, from the initial concept all the way to production, in a simplified and automated manner.

Our Networking Research group is seeking a forward-thinking Engineering Manager with a product leadership approach to manage a team of Software Engineers, AI Agent Developers, and Researchers.

In this role, you will bridge the gap between brand-new AI research and practical engineering. You will lead the charge in integrating autonomous AI agents into the daily workflows of our architects, hardware designers, and researchers. You will be responsible for understanding internal engineering bottlenecks, researching the latest academic and industry advancements, and translating them into deployed production tools using our AI stack.

What you'll be doing:

Team Leadership & Strategy: Lead and mentor a mixed team of SW engineers and researchers, balancing the agility of research with the responsibility of production software engineering.

Product Engineering: Partner with internal engineering teams (Hardware, Architecture, Research) to analyze their workflows, identify friction points, and architect Agentic AI solutions to accelerate their work.

Applied Research: Actively monitor the latest AI trends, academic papers, and technical blogs (LLMs, Agents, RAG). Translate these insights into concrete features and tools for the team.

Agent Architecture: Be responsible for the design of scalable frameworks for AI agent interaction, ensuring they can reason, plan, and orchestrate tasks within complex engineering environments.

SW-Focused MLOps: Define and implement the software lifecycle for AI models - from fine-tuning to deployment - utilizing our AI deployment tools and ensuring seamless CI/CD.

Production Deployment: Ensure reliable operation of agent fleets, optimizing for latency and resource consumption while maintaining security and access controls.