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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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Kompaniyadagi ish o‘rinlari

Senior AI Researcher – Hybrid in Israel

Lead reinforcement learning evaluation, red-teaming, and security audits for open-weight AI models. Build benchmarking and post-training pipelines for ActiveFence’s AI trust, safety, and security work.

ILIsroilGibridTo‘liq stavkaSeniorTalab Qilinadigan Til: inglizchaMashinaviy o‘qitish muhandisiSunʼiy intellekt tadqiqot olimi
DockerPyTorch

Tavsif

  • Lead post-training evaluations, red-team exercises, and reinforcement learning gym audits for open-weight models
  • Design rigorous benchmarking methodologies
  • Assess large language models against sophisticated threats, including Indirect Prompt Injection attacks
  • Build post-training evaluation pipelines that measure realistic frontier-level security capabilities
  • Run post-training experiments such as GRPO with open-weight generalist models in security-focused reinforcement learning environments
  • Examine live loss curves and rollout traces to detect reward hacking, lazy policy convergence, and poorly specified verifiers
  • Assess tasks and multi-turn environments involving tool use, web navigation, and computer use for realism, threat-model accuracy, data distribution, and dataset balance
  • Produce evaluation cards covering checkpoint performance gains, failure modes, rollout tokens and turns, and task-level success rates
  • Integrate Dockerized environments, including Harbor-format environments, into internal training frameworks
  • Improve reset and statefulness semantics, concurrency, and throughput limits

Talablar

  • M.S. or Ph.D. in Data Science, Machine Learning, Computer Science, or equivalent practical deep learning experience
  • Hands-on experience training large-scale models with reinforcement learning algorithms such as GRPO or PPO on open-weight architectures
  • Strong knowledge of LLM vulnerabilities, red-teaming practices, and defensive alignment against Indirect Prompt Injection attacks
  • Proficiency with PyTorch, Docker containerization, and distributed training architectures
  • Ability to interpret agent rollout traces, create deterministic rubrics and verifiers, and debug complex reward-shaping issues
  • Experience with standard reinforcement learning gym formats such as Harbor is preferred
  • Experience assessing complex agentic workflows in tool-use or web-browser environments is preferred
  • Familiarity with evaluating Llama-like or Mistral-like open-weight models against adversarial workloads is preferred

O‘xshash ish o‘rinlari

General Motors

Senior Embedded Software Engineer, General Motors

General Motors
H1B vizasi homiysiAvtomobilsozlikIshlab chiqarishTransport
USAmerika Qo‘shma ShtatlariMIMichiganGibridTo‘liq stavkaSeniorTalab Qilinadigan Til: inglizchaDasturiy ta’minot muhandisiFull-stack muhandisi
Kiberxavfsizlik

Develop embedded C and AUTOSAR controls software for General Motors, leading delivery across the software lifecycle. Test, debug, and improve supplier solutions in a hybrid role based in Milford, Michigan.

Ochish