דרושים » AI » AI Cybersecurity Researcher REF414Z

Requirements:
What you will bring
3+ years of experience in AI research, applied machine learning, or a related field.
Strong understanding of deep learning, neural networks, transformer architectures, and large language models.
Hands-on experience training, fine-tuning, evaluating, or post-training modern machine learning models.
Strong Python skills and experience with machine learning frameworks such as PyTorch.
Experience designing rigorous experiments and evaluating model performance.
Ability to translate research into production-ready systems.
Strong analytical thinking and excellent communication skills.

Nice to Have
Experience with AI security, AI safety, adversarial machine learning, or robust machine learning.
Experience developing or evaluating autonomous AI agents, reasoning models, or agentic workflows.
Experience with supervised fine-tuning, model optimization, post-training techniques, or reinforcement learning.
Publications in leading AI conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, etc.) or impactful applied research in industry.
Experience deploying machine learning models into production and building scalable AI-powered products.

Requirements:
5+ years of experience as an applied researcher with proven production-level impact.
Masters degree in Computer Science, Mathematics, Statistics, or Engineering (PhD is a plus, but not required).
Experience in applied research, including work with Transformers, open-source LLMs, or other advanced deep learning architectures.
Strong programming skills in Python, with hands-on experience in modern ML frameworks and tooling.
Excellent problem-solving skills and a strong experimental mindset.
Strong communication skills and the ability to collaborate effectively across teams.
High curiosity and a passion for learning new technologies, methods, and domains.

Requirements:
WHAT YOU WILL BRING
5+ years of professional experience as a research scientist, applied scientist, or algorithms/ML engineer.
Ph.D. or Masters degree in computer science, machine learning, data science, statistics, or a related field.
Expert-level proficiency in PyTorch and deep learning training and inference optimization.
Hands-on experience training, fine-tuning, evaluating, or optimizing large-scale LLMs in research or production environments.
Strong Python programming skills and experience writing production-grade ML code.
Experience working with large-scale data and ML infrastructure.


NICE TO HAVE
Experience applying AI/ML to cybersecurity, including threat detection, incident response, log analysis, or related problems.
Experience with reinforcement learning or preference optimization for LLMs.
A track record of research publications, open-source contributions, or other technical work demonstrating research depth.

Requirements:
Requirements:
Deep learning expertise with a track record of non‑trivial research (industry or academia) in LLMs or other domains (e.g., CV, speech). We care that youve changed models or methods in meaningful ways (architecture/training/eval), not just used them.
Strong experimental design and scientific writing; comfort pre‑registering hypotheses, testing causal claims, proposing novel directions in a fast-changing field.
PhD or equivalent research experience in the industry (5+ years in a leading research team). Publication record or a portfolio of high‑impact open artifacts will make you stand out from the crowd.
Familiarity with AI frameworks (e.g., HuggingFace Transformers, LangChain, scikit-learn, PyTorch); Experience with a production grade codebase with several contributors is a bonus.
Experience in data analysis: visualization, exploration, cleanup.
Knowledge in GenAI tools such as LLM Orchestrations and integration packages, Agents, RAG systems - a bonus.

Requirements:
Required Qualifications
Minimum of 5 years of experience in Applied AI research, Data Science, or Machine Learning engineering.
Deep, hands-on experience over the past 12+ months developing leading-edge agentic coding practices.
Proficiency in Python and/or Typescript, with deep familiarity in Agentic AI orchestration libraries like LangChain, LlamaIndex, or LangGraph.
Experience with automated statistical evaluation to track model drift, latency, costs, and hallucination rates.
Hands-on experience developing advanced RAG pipelines, GraphRAG frameworks, and interacting with Graph Databases or vector indices.
Practical experience with autonomous agent protocols, multi-agent communication layers, or Model Context Protocol (MCP).
Preferred Qualifications
Understanding of secure data pipelines, including Role-Based Access Control (RBAC) and prompt injection mitigation.
Hands-on production experience utilizing Google Cloud Platform (GCP) services and infrastructure.
Strong knowledge of data privacy and security considerations specific to AI and ML systems.
Post-graduate work or equivalent advanced degree in Artificial Intelligence, Data Science, or a related field.

Requirements:
Required Qualifications:
Minimum of 5 years of experience in Applied AI research, Data Science, or Machine Learning engineering.
Deep, hands-on experience over the past 12+ months developing leading-edge agentic coding practices.
Proficiency in Python and/or Typescript, with deep familiarity in Agentic AI orchestration libraries like LangChain, LlamaIndex, or LangGraph.
Experience with automated statistical evaluation to track model drift, latency, costs, and hallucination rates.
Hands-on experience developing advanced RAG pipelines, GraphRAG frameworks, and interacting with Graph Databases or vector indices.
Practical experience with autonomous agent protocols, multi-agent communication layers, or Model Context Protocol (MCP).
Preferred Qualifications:
Understanding of secure data pipelines, including Role-Based Access Control (RBAC) and prompt injection mitigation.
Hands-on production experience utilizing Google Cloud Platform (GCP) services and infrastructure.
Strong knowledge of data privacy and security considerations specific to AI and ML systems.
Post-graduate work or equivalent advanced degree in Artificial Intelligence, Data Science, or a related field.

Requirements:
Academic background: B.Sc. in an engineering or quantitative scientific field, with coursework or focus in data science, machine learning, or information systems (or equivalent experience).
Experience: 5+ years of hands-on experience in applied AI research, data engineering, data science, or applied ML - ideally with security-related domains.
Strong Python & Data Pipelines: Proven ability to build robust pipelines that handle large-scale, real-world data.
LLM Systems in Production: Experience building and shipping real product features powered by LLMs, taking solutions from prototype to production, owning reliability through evaluation and monitoring.
Cyber Security Domain Knowledge: Familiarity with core security concepts, including asset inventory and context, vulnerability management and risk prioritization, exposure/attack-surface analysis, network and traffic analysis, and practical remediation workflows in collaboration with security teams.
Experiments & Workflow Design: Experience designing and running experiments and building reproducible end-to-end data workflows to validate new product features and iterate based on measurable results.
Problem Solving Skills: Strong analytical and critical-thinking skills, with the ability to break down ambiguous problems, apply sound algorithmic reasoning, and develop creative, pragmatic solutions.
Startup Mindset: High ownership, strong collaboration, and the ability to execute quickly and effectively in a fast-paced, evolving environment.
Advantage
Experience working with security datasets and real-world enterprise environments.
Experience building structured extraction and normalization systems using LLMs.
Strong product intuition: ability to turn AI capabilities into features users trust and rely on.
Experience designing AI monitoring systems (quality drift, and evaluation pipelines).

Requirements:
MSc or PhD in Computer Science, Mathematics, Physics, Electrical Engineering, or a closely related field.

Proficiency in Python.

Strong theoretical background in machine learning and deep learning principles.

Solid understanding of NLP concepts and relevant linguistic principles (co-reference, natural language inference, grammatical relations, and similar).

Experience using statistics and information theory to evaluate AI models and conduct EDA.

4+ years of experience conducting NLP research with deep learning models and frameworks (PyTorch, TensorFlow, and similar).

Experience shipping AI systems from research to production.

Experience with conversational AI agents is a big plus.

Publications at top-tier conferences (ACL, EMNLP, NeurIPS, ICML, CVPR) are a plus.

Requirements:
5+ years of experience in data analysis, product analytics, or a similar analytical role, including senior or lead-level scope.
Strong SQL skills and proven experience working with large datasets.
Proficiency with Python (or similar) for data analysis and automation.
Hands-on experience with GenAI tools, LLMs, and AI agents, and a good understanding of how they behave.
Experience analyzing the output and quality of AI or ML-driven products.
A track record of translating data into insights that influence product decisions and deliver measurable impact.
Ability to communicate findings clearly to technical and non-technical stakeholders, and to collaborate with and influence cross-functional teams.
Preferred
Experience measuring the quality or performance of AI systems.
Experience at a fast-growing product or SaaS company.
Familiarity with the security domain - threat modeling, application security, or related fields.
Experience mentoring analysts or setting analytical standards on a team.

Requirements:
Masters/Ph.D. in Computer Science, Electrical Engineering, Machine Learning, information systems engineering, or related field.
3+ years of applied AI/ML experience with a focus on Generative AI, LLMs, and deep learning.
Depth in agentic system design, retrieval-augmented generation, context and tool engineering, and rigorous evaluation.
Familiarity with AI frameworks (e.g., HuggingFace Transformers, LangChain, scikit-learn, PyTorch)
Familiarity with embedding models and vector databases
Exceptional problem-solving, creative thinking, and analytical skills.
Experience with voice AI technologies, speech recognition, or text-to-speech systems - strong advantage.