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ActiveFence
ActiveFence
201 – 500 Xodimlar
KiberxavfsizlikSun’iy intellekt
ActiveFence onlayn platformalar uchun ishonch va xavfsizlikka ixtisoslashgan kiberxavfsizlik hamda sun’iy intellekt kompaniyasidir. Kompaniyaning to‘liq texnologik yechimi razvedka tadqiqotlari, sun’iy intellekt asosida zararli kontentni aniqlash va kontentni moderatsiya qilish vositalarini birlashtirib, platformalarga bolalarni ekspluatatsiya qilish, dezinformatsiya, nafrat tili, terrorizm, yalang‘ochlik, firibgarlik va onlayn muhitdagi boshqa zararlar kabi xatarlarni bartaraf etishga yordam beradi. ActiveFence turli hajmdagi ishonch va xavfsizlik jamoalarini qo‘llab-quvvatlab, butun dunyo bo‘ylab 100 dan ortiq tilda uch milliarddan ziyod foydalanuvchini himoya qilishga ko‘maklashadi.
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Senior GenAI Data Scientist at ActiveFence (Hybrid, Israel)

Senior GenAI Data Scientist role focused on automated red teaming, machine learning, and large-scale data analysis. Build systems that help identify emerging security threats across online experiences and AI products.

ILIsroilGibridTo‘liq stavkaSeniorTalab Qilinadigan Til: inglizchaMa’lumotlar olimi
ApacheETLHadoopPythonPyTorchScikit-LearnSparkSQL

Tavsif

  • Develop scalable data-science agents that reason through and execute multi-step workflows for generating and analyzing adversarial attacks.
  • Manage and interpret prompt and response data from red-team campaigns, model evaluations, and synthetic attacks, including cleaning, curation, normalization, and labeling.
  • Examine structured and unstructured data to identify trends, clusters, anomalies, attack families, jailbreak techniques, and novel patterns.
  • Create machine-learning models and predictive algorithms that automate red-team processes.
  • Develop evaluation and monitoring pipelines to measure attack success and compare model behavior across providers.
  • Apply statistical methods and experiments to verify findings and support accurate, reproducible results.
  • Move research into production through reliable, scalable systems designed for cost and latency efficiency, supported by testing, CI/CD, and telemetry.

Talablar

  • At least five years of hands-on data-science or machine-learning experience using Python or R and SQL.
  • At least three years of experience with Scikit-Learn and PyTorch.
  • Practical experience in GenAI red teaming, adversarial testing, or LLM security.
  • Experience building and deploying LLM-powered or machine-learning systems in production.
  • Strong knowledge of LLM design patterns, including prompt engineering, tool calling, structured outputs, and RAG.
  • Strong understanding of clustering, embeddings and NLP, semantic similarity, and anomaly or novelty detection.
  • Experience developing classification, ranking, or prioritization systems.
  • Solid foundation in statistics, experimental design, and model evaluation.
  • Experience with Cradle, Apache Hadoop, Spark, and large-scale ETL and data-pipeline development.
  • M.Sc. in computer science, electrical engineering, mathematics, statistics, or a related discipline.
  • A Ph.D. is advantageous but not required.

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