AI Research Team Lead

Cyera Cyera · Vertical AI · Tel Aviv, Israel · Research

Lead and mentor an AI research team focused on data security, driving strategy, and overseeing the end-to-end research lifecycle from problem framing to production deployment. Develop and evaluate deep learning and NLP solutions, including agentic workflows and ML models for classification and analysis, ensuring rigorous evaluation and alignment with business objectives.

What you'd actually do

  1. Lead and mentor a team of AI researchers, fostering a culture of excellence and innovation while overseeing the end-to-end research lifecycle.
  2. You will act as the technical and strategic lead, defining team priorities, roadmap, and data science methodologies.
  3. Mentor and grow team members through technical guidance, career development, and peer reviews.
  4. Collaborate with cross-functional leadership in Product and Engineering to align research efforts with core business objectives and customer needs.
  5. Manage team performance, resource allocation, and timely project delivery within an agile environment.

Skills

Required

  • BSc in computer science, math, physics, or a related field
  • 7+ years of experience as an AI Researcher/NLP Researcher/Applied Scientist
  • Experience leading or managing research teams
  • Proven track record of building and managing high-performing AI research / Data Science teams
  • Solid grounding in core machine and deep learning concepts and techniques
  • Data challenges (imbalance, scaling etc.)
  • Evaluation techniques
  • Applying LLMs
  • Prompt engineering
  • Prompt tuning (few-shot, chain-of-thought, tool/function calling, routing)
  • Task adaptation (instruction/SFT, PEFT/LoRA, DPO/RLHF)
  • Retrieval-augmented generation
  • Rigorous evaluation
  • Production deployment
  • Safety controls
  • Latency controls
  • Cost controls
  • Self-learner
  • Initiator
  • Able to quickly learn new technologies

Nice to have

  • Experience in NLP

What the JD emphasized

  • Proven track record of building and managing high-performing AI research / Data Science teams
  • Demonstrated expertise in applying LLMs - prompt engineering and prompt tuning (few-shot, chain-of-thought, tool/function calling, routing), task adaptation (instruction/SFT, PEFT/LoRA, DPO/RLHF), retrieval-augmented generation, rigorous evaluation and production deployment with appropriate safety, latency, and cost controls

Other signals

  • leading a team of AI researchers
  • setting strategic vision for research initiatives
  • driving team-wide project goals
  • partnering with Product and Engineering leadership
  • turning open-ended data-security challenges into measurable experiments and shipped features
  • managing the critical balance between research exploration and product delivery
  • owning the end-to-end lifecycle—from problem framing and data strategy to evaluation, deployment, and ongoing monitoring
  • defining team priorities, roadmap, and data science methodologies
  • mentoring and growing team members
  • aligning research efforts with core business objectives and customer needs
  • managing team performance, resource allocation, and timely project delivery
  • developing, evaluating, and maintaining deep learning and NLP solutions
  • designing and architecting production-grade agentic workflows
  • establishing rigorous evaluation pipelines to benchmark agent accuracy, latency, and cost
  • implementing ML models to the entire research process - clustering, text extraction, document analysis, and tabular data classification
  • accelerating the path from research to production
  • 7+ years of experience as an AI Researcher/NLP Researcher/Applied Scientist, including experience leading or managing research teams
  • Proven track record of building and managing high-performing AI research / Data Science teams
  • Solid grounding in core machine and deep learning concepts and techniques, data challenges (imbalance, scaling etc.), and evaluation
  • Demonstrated expertise in applying LLMs - prompt engineering and prompt tuning (few-shot, chain-of-thought, tool/function calling, routing), task adaptation (instruction/SFT, PEFT/LoRA, DPO/RLHF), retrieval-augmented generation, rigorous evaluation and production deployment with appropriate safety, latency, and cost controls
  • Experience in NLP - a significant advantage