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Location: Ramat Gan
Job Type: Full Time
We are looking for a Principal AI Security Researcher with deep, hands-on expertise in AI red teaming and AI security to lead and scale RLGyms AI security research efforts.
This is not a traditional cybersecurity leadership role. We need someone who has directly worked on breaking, attacking, testing, and securing AI systems, particularly large language models (LLMs), generative AI applications, and agentic AI systems.
The ideal candidate has significant practical experience designing and executing AI red-team programs, developing adversarial attacks, identifying AI-specific vulnerabilities, building automated red-teaming capabilities, and translating findings into effective guardrails and security protections.
What you'll be doing:
Lead and scale multidisciplinary teams focused on AI red teaming, adversarial testing, and AI security research.
Design and execute sophisticated attacks against LLMs, GenAI applications, and agentic AI systems.
Research attack techniques including prompt injection, jailbreaks, indirect prompt injection, tool abuse, agent manipulation, data leakage, model misuse, and adversarial behavior.
Build and evolve AI red-teaming engines and automated adversarial testing systems, including RLGym.
Building and scaling global AI security teams (red teaming, adversarial research, guardrails engineering) while fostering an innovation-driven security culture.
Overseeing advanced adversarial evaluations for GenAI models, agentic AI, and multi-agent (A2A) systems
Defining and implementing AI red-teaming frameworks aligned with OWASP AI Security guidelines, MITRE ATLAS, and NIST AI RMF, and operationalizing automated red-team engines to continuously stress-test models at scale.
Partnering with product and engineering to design and deploy enterprise-ready AI guardrails - including policy enforcement layers, monitoring pipelines, and anomaly detection systems - and championing secure deployment practices for GenAI (including agent orchestration via MCP and A2A workflows).
Requirements:
What we need to see:
Extensive leadership experience managing and scaling security or R&D organizations, with a strong track record of building high-performance teams and driving complex projects to completion.
Deep expertise in cybersecurity and AI - proven understanding of AI threats, adversarial machine learning, LLM vulnerabilities, and AI safety frameworks (OWASP Top 10 for LLMs, NIST AI Risk Management Framework, etc.).
Strategic mindset and execution skills, with the ability to set vision and direction for AI security initiatives and also dive into technical details when needed.
Excellent communication and collaboration abilities, including experience working cross-functionally with product, engineering, and compliance teams, and conveying technical concepts to executive stakeholders.
5+ years of relevant industry experience in cybersecurity, machine learning security, or related fields (with a focus on enterprise-scale products and AI systems).
Ways to stand out from the crowd:
Demonstrated thought leadership in AI security - for example, publishing research, speaking at industry events (Black Hat, DEF CON, OWASP Global AppSec), or contributing to AI security standards and open-source projects.
Experience building or deploying AI security products and tools, such as red teaming automation platforms, guardrail frameworks, or AI monitoring and anomaly detection systems.
Hands-on familiarity with agentic AI frameworks and protocols (e.g. LangChain, AutoGen, MCP, A2A) and cloud-based AI environments, showing you understand how to secure complex AI orchestration workflows.
A background in AI trust and safety or adversarial ML research, with insight into emerging threats and mitigation techniques for GenAI applications.
This position is open to all candidates.
 
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דרושים בוואן פתרונות טכנולוגיים בע"מ
מיקום המשרה: רמת גן
סוג משרה: משרה מלאה
לארגון גדול ומוביל דרוש/ה מדען/ית נתונים בכיר/ה לתפקיד Tech Lead בתחום הבינה המלאכותית הגנרטיבית (GenAI).
הובלה מקצועית וטכנולוגית של פתרונות data Science, NLP ו-GenAI.
תכנון ופיתוח ארכיטקטורות AI מקצה לקצה.
פיתוח והובלת מערכות RAG, כולל Embeddings, אינדוקס, חיפוש וקטורי, Retrieval ו-Reranking.
תכנון ופיתוח פתרונות SQL-to-Text / Natural Language to SQL.
פיתוח שכבות סמנטיות המחברות בין מאגרי מידע, מודלי שפה, Agents ומערכות ארגוניות.
עבודת Hands-On בפיתוח רכיבי ליבה, אבות טיפוס, Agents ותהליכי עיבוד והערכה.
עבודה עם Python ו-SQL, מסדי נתונים וקטוריים, מנועי חיפוש ומאגרי מידע מובנים ולא מובנים.
בניית תהליכי Evaluation ומדדים להערכת ביצועי מערכות GenAI.
שילוב תהליכי MLOps / DataOps / LLMOps והעברת פתרונות לסביבת Production.
הובלת פרויקטים טכנולוגיים מורכבים ועבודה מול צוותי פיתוח, data, ארכיטקטים, מנהלי מוצר וספקים טכנולוגיים.
הנחיה מקצועית, Code Review והובלת אנשי מקצוע בתחום.
דרישות:
7 שנות ניסיון ומעלה בתפקידי data, מתוכן לפחות 3 שנות ניסיון בתחום data Science.
תואר שני ומעלה באחד מהתחומים: הנדסה, מדעי המחשב, סטטיסטיקה, חקר ביצועים, מדעי הנתונים או מתמטיקה.
או
תואר שלישי כלשהו + 9 שנות ניסיון ומעלה.
יתרון משמעותי לניסיון ב:
פיתוח והעברה לייצור של מערכות NLP ואחזור מידע.
פיתוח מערכות RAG ופתרונות מבוססי LLM.
SQL-to-Text / Natural Language to SQL.
Python ו-SQL ברמה גבוהה.
Vector Databases, Knowledge Graphs ומנועי חיפוש.
Prompt Engineering ו-Agentic AI.
MLOps / DataOps / LLMOps.
ארכיטקטורת מערכות AI בסביבות ענן.
הובלת פרויקטים מורכבים משלב ה-POC ועד Production. המשרה מיועדת לנשים ולגברים כאחד.
 
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27/07/2026
חברה חסויה
Location: Ramat Gan
Job Type: Full Time
We are seeking a highly motivated AI Researcher to join our core Data Science team. In this role, you will be at the forefront of tackling complex, high-impact business challenges by leveraging cutting-edge GenAI technologies, advanced AI research, and classical machine learning and statistical modeling.
You will not just be building models; you will be driving research from ideation to production. We are looking for a true self-learner, someone who thrives in an environment that demands both a strong sense of ownership over your deliverables and deep collaboration. You will work closely with fellow researchers, product managers, and software developers to translate abstract business problems into scalable data-driven solutions.
If you are passionate about staying ahead of the AI curve, building agentic workflows, and proactively driving your research initiatives forward within a collaborative team structure, this is the perfect role for you.
What You Will Do:
End-to-End Research & Ownership: Lead targeted research projects from initial hypothesis through to production-ready solutions. Take strong ownership of your work's performance and partner with engineering to ensure successful, scalable integration
Applied AI Research & Integration: Lead research into LLMs, agentic systems, and modern AI methods, then design and build the workflows that bring them into our ecosystem.
LLM Evaluation & Optimization: Contribute to and establish rigorous evaluation frameworks for LLMs to ensure accuracy, safety, and business alignment in practical applications.
Cross-Functional Collaboration: Act as a bridge between data, engineering, and product. Work with Developers to ensure seamless integration of ML features into the core product.
Continuous Innovation: Act as an internal advocate for emerging AI research and methodologies. Continuously read, experiment, and implement state-of-the-art techniques to advance our research agenda and accelerate research velocity.
Requirements:
3 to 5 years of proven industry experience working as an AI Researcher or Data Scientist in a fast-paced environment.
M.Sc. in Data Science, Computer Science, Statistics, Mathematics, or a closely related quantitative field.
Deep theoretical knowledge and hands-on experience with classical Machine Learning algorithms and advanced statistical modeling techniques.
Proven experience researching and building with advanced AI concepts, such as RAG (Retrieval-Augmented Generation) pipelines, agentic workflows, autonomous AI systems, and rigorous LLM evaluation mechanisms.
Strong experience working with modern AI productivity and development tools (e.g., Claude Code, Cursor, GitHub Copilot, advanced prompting frameworks) to accelerate coding and research execution.
A demonstrated ability to teach yourself new concepts quickly. You proactively research, test, and propose state-of-the-art solutions rather than waiting for a rigid roadmap.
Excellent ability to communicate complex mathematical and technical concepts to non-technical stakeholders while matching the technical depth required to work seamlessly with Engineering.
Advantages:
While the core requirements above are essential, the following will make your application stand out:
Familiarity with or prior experience working in the Finance domain (e.g., risk modeling, algorithmic trading, fraud detection, financial time-series forecasting).
Knowledge and practical experience with causal inference techniques to measure true business impact beyond standard correlation.
Experience designing and training deep neural networks (e.g., PyTorch, TensorFlow) for complex unstructured data tasks.
This position is open to all candidates.
 
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20/08/2026
חברה חסויה
Location: Ramat Gan
Job Type: Full Time and Hybrid work
We are seeking a highly motivated and experienced LLM/ML Agentic AI Researcher to lead the technical development of our agentic AI interpretation framework. This hands-on role involves designing, building, and evaluating AI agents that interpret complex biological data.

You will be at the forefront of developing a sophisticated scientific reasoning system that leverages Large Language Models (LLMs) to provide structured, biologically-grounded explanations. Collaborating closely with immunologists, machine learning researchers, and technical leadership, you'll shape how we derive insights at a systems level, pushing the boundaries of AI in biology.

Location: Ramat Gan, Israel (Hybrid role)

What will you do?
Design, prototype, and build LLM-based agentic systems that reason over biological data, scientific literature, model outputs, and internal tools.
Develop agents capable of structured reasoning, hypothesis generation, explanation, planning, tool use, and iterative scientific analysis.
Build robust evaluation frameworks for agentic systems, including automated and human-in-the-loop evaluation pipelines.
Define and implement benchmarks, metrics, and test suites for measuring agent performance, including reasoning quality, biological grounding, factuality, robustness, reproducibility, and usefulness.
Work closely with AI researchers, computational biologists, immunologists, and product teams to translate scientific needs into measurable AI capabilities.
Create evaluation datasets and benchmark tasks that reflect real-world biological and therapeutic reasoning problems.
Analyze agent behavior, failure modes, hallucinations, tool-use errors, reasoning gaps, and grounding issues.
Contribute to the architecture of production-grade AI systems, including agent orchestration, retrieval, tool calling, memory, planning, and monitoring.
Stay up to date with the latest developments in LLMs, agentic AI, evaluation methodologies, and scientific AI systems.
Help turn research prototypes into reliable products used by internal teams and external partners.
Requirements:
Required qualifications:
MSc or PhD in Computer Science, Electrical Engineering, Computational Biology, Statistics, Mathematics, or a related quantitative field.
Strong background in machine learning, data science, statistics, or computational modeling.
Hands-on experience building with LLMs and agentic AI systems.
Proven ability to design evaluation methodologies for AI systems, especially LLM-based or agent-based systems.
Experience working with LLM APIs such as OpenAI, Anthropic, Google, or open-source LLMs.
Experience with agent frameworks or orchestration tools such as LangGraph, LangChain, or similar systems.
Experience defining benchmarks, metrics, validation sets, scoring methods, or automated evaluation pipelines.
Strong Python skills and ability to write clean, production-aware research code.
Ability to work with complex, noisy, high-dimensional data.
Strong communication skills and ability to collaborate with experts from different disciplines.

Desired personal traits:
You want to make an impact on humankind.
You prioritize We over I.
You enjoy getting things done and striving for excellence.
You collaborate effectively with people of diverse backgrounds and cultures.
You have a growth mindset.
You are candid, authentic, and transparent.
This position is open to all candidates.
 
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מודים לך שלקחת חלק בשיפור התוכן שלנו :)
20/08/2026
חברה חסויה
Location: Ramat Gan
Job Type: Full Time and Hybrid work
We are looking for a Senior AI Researcher to join our foundation models team and help advance large-scale models for single-cell RNA (scRNA) data.

In this role, you will focus on training and improving transformer-based and deep learning models, developing algorithmic solutions to challenging modeling problems, and adapting modern foundation model techniques to biological data. This is a hands-on research position for someone who enjoys building and training deep models, understanding their behavior, and pushing them to perform better.

You do not need prior biology experience - but you should be excited to apply your deep learning expertise to a new scientific domain.

Location: Ramat Gan, Israel (hybrid model)

What will you do?
Develop, train, and refine transformer-based and related architectures for large-scale scRNA data.
Tackle challenging modeling problems with thoughtful, principled approaches.
Apply ideas from NLP and vision (pretraining, self-supervision, transfer learning) to biological data.
Plan careful experiments, perform ablations, and deeply analyze results to guide model improvements.
Engage directly with large, noisy biological datasets and build intuition for model behavior in this setting.
Partner with engineers, data scientists, and biology experts to turn research ideas into working systems.
Requirements:
Required qualifications:
MSc or PhD in Computer Science, Mathematics, Physics, or a related scientific field.
5+ years of hands-on, industry experience training deep learning models (e.g., transformers, vision models, or related architectures).
Experience with large-scale pretraining and foundation models.
Strong engineering background, specifically building machine learning infrastructure for training large models.
Strong practical experience with PyTorch (or similar frameworks).
Solid understanding of optimization and representation learning.
Strong experience in experimental design, statistical evaluation, and working with large, complex datasets.
Ability and willingness to adapt to a new domain (biology).

Preferred qualifications:
Experience adapting methods across domains.
Research publications or meaningful research contributions.
Experience in computational biology or biomedical data (a plus, not required).

Desired personal traits:
You want to make an impact on humankind.
You prioritize We over I.
You enjoy getting things done and striving for excellence.
You collaborate effectively with people of diverse backgrounds and cultures.
You have a growth mindset.
You are candid, authentic, and transparent.
This position is open to all candidates.
 
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